Underwater image recovery considering polarization effects of objects

Image recovery method for underwater targets with complex polarization characteristics

Polarization imaging techniques have been effective in improving the clarity of turbid underwater images affected by water scattering. These techniques offer valuable additional information compared to traditional methods. However, previous descattering methods have mostly been designed for targets with uniform distribution of polarimetric characteristics. Therefore, targets with complex polarization characteristics have non-uniform distribution of polarization characteristics when dealing with such problems, additional parameter estimation errors can limit the results of image recovery. This paper proposes what we believe is a novel approach to address this issue. The method involves obtaining a new set of images using the polarization images vector space transformation method. The angle of polarization (AOP) of the target reflected light is estimated using the additivity law of the Stokes vector. This information is then combined with the physical model of underwater imaging to recover the underwater images affected by scattering. Experimental results conducted under varying levels of water turbidity validate the effectiveness of the proposed method. Moreover, the proposed method significantly broadens the range of application scenarios.

Unsupervised underwater imaging based on polarization and binocular depth estimation

Scattering caused by suspended particles in the water severely reduces the radiance of the scene. This paper proposes an unsupervised underwater restoration method based on binocular estimation and polarization. Based on the correlation between the underwater transmission process and depth, this method combines the depth information and polarization information in the scene, uses the neural network to perform global optimization and the depth information is recalculated and updated in the network during the optimization process, and reduces the error generated by using the polarization image to calculate parameters, so that detailed parts of the image are restored. Furthermore, the method reduces the requirement for rigorous pairing of data compared to previous approaches for underwater imaging using neural networks. Experimental results show that this method can effectively reduce the noise in the original image and effectively preserve the detailed information in the scene.

Polarization angle information enhancement method based on polarimetric array imaging

Polarization imaging, based on the measurement of polarization parameters containing specific physical information, has found extensive applications across various domains. Among these parameters, polarization angle information plays a crucial role in revealing texture details. However, in practical scenarios, noise during image acquisition can lead to significant degradation of polarization angle information. To address this issue, we introduce a novel, to the best of our knowledge, polarization angle information enhancement method based on polarimetric array imaging. Our proposed method utilizes the principles of polarimetric array imaging to effectively restore texture information embedded within polarization angle images. Through the deployment of a self-designed polarimetric array imaging system, we conducted experiments in diverse scenes to validate the efficacy of our approach. The acquired polarization angle data were subjected to our method for enhancement. The experimental outcomes distinctly illustrate the noise suppression capabilities of our method, showcasing its ability to faithfully reconstruct intricate details obscured by substantial noise interference.

Dynamic polarization fusion network (DPFN) for imaging in different scattering systems

Deep learning has broad applications in imaging through scattering media. Polarization, as a distinctive characteristic of light, exhibits superior stability compared to light intensity within scattering media. Consequently, the de-scattering network trained using polarization is expected to achieve enhanced performance and generalization. For getting optimal outcomes in diverse scattering conditions, it makes sense to train expert networks tailored for each corresponding condition. Nonetheless, it is often unfeasible to acquire the corresponding data for every possible condition. And, due to the uniqueness of polarization, different polarization information representation methods have different sensitivity to different environments. As another of the most direct approaches, a generalist network can be trained with a range of polarization data from various scattering situations, however, it requires a larger network to capture the diversity of the data and a larger training set to prevent overfitting. Here, in order to achieve flexible adaptation to diverse environmental conditions and facilitate the selection of optimal polarization characteristics, we introduce a dynamic learning framework. This framework dynamically adjusts the weights assigned to different polarization components, thus effectively accommodating a wide range of scattering conditions. The proposed architecture incorporates a Gating Network (GTN) that efficiently integrates multiple polarization features and dynamically determines the suitable polarization information for various scenarios. Experimental result demonstrates that the network exhibits robust generalization capabilities across continuous scattering conditions.

Underwater image enhancement via red channel maximum attenuation prior and multi-scale detail fusion

The underwater environment poses great challenges, which have a negative impact on the capture and processing of underwater images. However, currently underwater imaging systems cannot adapt to various underwater environments to guarantee image quality. To address this problem, this paper designs an efficient underwater image enhancement approach that gradually adjusts colors, increases contrast, and enhances details. Based on the red channel maximum attenuation prior, we initially adjust the blue and green channels and correct the red channel from the blue and green channels. Subsequently, the maximum and minimum brightness blocks are estimated in multiple channels to globally stretch the image, which also includes our improved guided noise reduction filtering. Finally, in order to amplify local details without affecting the naturalness of the results, we use a pyramid fusion model to fuse local details extracted from two methods, taking into account the detail restoration effect of the optical model. The enhanced underwater image through our method has rich colors without distortion, effectively improved contrast and details. The objective and subjective evaluations indicate that our approach surpasses the state-of-the-art methods currently. Furthermore, our approach is versatile and can be applied to diverse underwater scenes, which facilitates subsequent applications.

Investigation polarimetric scattering of light from the randomly rough surface based on the calculation of the Mueller matrix

As the transmission matrix of scattering and incident light, the Mueller matrix reflects the polarimetric scattering characteristics of the rough surface, providing a significant reference for the study of light scattering. Currently, few calculations of the Mueller matrix for a two-dimensional randomly rough surface have been carried out by numerical methods. In this paper, we use six polarization states of incident light and calculate their scattering polarization states numerically by finite-difference time-domain method and obtain the rough surface Mueller matrix by combination. To verify the accuracy of the calculated Mueller matrix, the polarization state of the scattering light obtained by simulation is compared with the predicted result, and the maximum relative error is 0.0635, yielding a good result. In addition, we use this method to obtain the Mueller matrix at different incidence angles and investigate the polarization scattering characteristics. The results show that the derived parameters of the Mueller matrix of different media at different incidence angles have distinct trends. This polarization scattering property obtained from the Mueller matrix can be effectively applied to target recognition, material detection, and other fields.

Instability-driven image recovery of 180-degree backscattered polarized-light in turbid water

Although the incoherent modulation instability has been proven to be effective for the recovery of forward-scattering images, the similar attempt of backscatter is still non-ideal. In this paper, considering the preservation properties of polarization and coherence in 180° backscatter, we propose an instability-driven nonlinear imaging method based on polarization modulation. A coupling model is established using Mueller calculus and mutual coherence function, in which the instability generation and image reconstruction are both analyzed. Experimental results clearly show the enhancement of imaging quality. This method is general and has potential for echo detection in various scattering environments.

Underwater active polarization descattering based on a single polarized image

Active polarization imaging techniques have tremendous potential for a variety of underwater applications. However, multiple polarization images as input are necessary for almost all methods, thereby limiting the range of applicable scenarios. In this paper, via taking full advantage of the polarization feature of target reflective light, the cross-polarized backscatter image is reconstructed via introducing an exponential function for the first time, only based on mapping relations of co-polarized image. Compared with rotating the polarizer, the result performs a more uniform and continuous distribution of grayscale. Furthermore, the relationship of degree of polarization (DOP) between the whole scene and backscattered light is established. This leads to an accurate estimation of backscattered noise and high-contrast restored images. Besides, single-input greatly simplifies the experimental process and upgrades efficiency. Experimental results demonstrate the advancement of the proposed method for objects with high polarization under various turbidities.

Underwater Target Detection Utilizing Polarization Image Fusion Algorithm Based on Unsupervised Learning and Attention Mechanism

Since light propagation in water bodies is subject to absorption and scattering effects, underwater images using only conventional intensity cameras will suffer from low brightness, blurred images, and loss of details. In this paper, a deep fusion network is applied to underwater polarization images; that is, the underwater polarization images are fused with intensity images using the deep learning method. To construct a training dataset, we establish an experimental setup to obtain underwater polarization images and perform appropriate transformations to expand the dataset. Next, an end-to-end learning framework based on unsupervised learning and guided by an attention mechanism is constructed for fusing polarization and light intensity images. The loss function and weight parameters are elaborated. The produced dataset is used to train the network under different loss weight parameters, and the fused images are evaluated based on different image evaluation metrics. The results show that the fused underwater images are more detailed. Compared with light intensity images, the information entropy and standard deviation of the proposed method increase by 24.48% and 139%. The image processing results are better than other fusion-based methods. In addition, the improved U-net network structure is used to extract features for image segmentation. The results show that the target segmentation based on the proposed method is feasible under turbid water. The proposed method does not require manual adjustment of weight parameters, has faster operation speed, and has strong robustness and self-adaptability, which is important for research in vision fields, such as ocean detection and underwater target recognition.

Fast image visibility enhancement based on active polarization and color constancy for operation in turbid water

Vehicles operating in a water medium sometimes encounter harsh conditions with high turbidity and low scene illumination, making it challenging to obtain reliable target information through optical devices. Although many post-processing solutions were proposed, they are not applicable to continuous vehicle operations. Inspired by the advanced polarimetric hardware technology, a joint fast algorithm was developed in this study to address the above problems. Backscatter attenuation and direct signal attenuation were solved separately by utilizing the revised underwater polarimetric image formation model. A fast local adaptive Wiener filtering method was used to improve the backscatter estimation by reducing the additive noise. Further, the image was recovered using the fast local space average color method. By using a low-pass filter guided by the color constancy theory, the problems of nonuniform illumination caused by artificial light and direct signal attenuation were both addressed. The results of testing on images from laboratory experiments showed improved visibility and realistic chromatic rendition.

Event-based imaging polarimeter simulation with a single DoFP image

An event camera is a neuromorphic vision sensor with a high dynamic range (HDR), high temporal resolution (HTR), low latency, and low power consumption. A polarimeter is an instrument for measuring the state of polarization of light. Currently, most imaging polarimeters are limited in dynamic range and frame rate when used with frame-based cameras. We can establish an event-based imaging polarimeter using the principle of the event camera to obtain HDR and HTR polarized event streams for processing polarization information. However, because of the short history and high cost of event cameras, developing an event-based imaging polarimeter requires substantial resources. We propose an event-based imaging polarimeter simulation method with a single division of focal plane image based on the existing research on event simulation. This method can easily convert existing data into a polarized event stream. It is beneficial to lower the requirement of processing polarized event streams and to create large datasets for deep learning.

Self-attention module in a multi-scale improved U-net (SAM-MIU-net) motivating high-performance polarization scattering imaging

Polarization imaging has outstanding advantages in the field of scattering imaging, which still encounters great challenges in heavy scattering media systems even though there are helps from deep learning technology. In this paper, we propose a self-attention module (SAM) in multi-scale improved U-net (SAM-MIU-net) for the polarization scattering imaging, which can extract a new combination of multidimensional information from targets effectively. The proposed SAM-MIU-net can focus on the stable feature carried by polarization characteristics of the target, so as to enhance the expression of the available features, and make it easier to extract polarization features which help to recover the detail of targets for the polarization scattering imaging. Meanwhile, the SAM's effectiveness has been verified in a series of experiments. Based on proposed SAM-MIU-net, we have investigated the generalization abilities for the targets' structures and materials, and the imaging distances between the targets and the ground glass. Experimental results demonstrate that our proposed SAM-MIU-net can achieve high-precision reconstruction of target information under incoherent light conditions for the polarization scattering imaging.

Underwater polarization imaging for visibility enhancement of moving targets in turbid environments

Polarization imaging techniques have more prominent advantages for imaging in strongly scattered media. Previous de-scattering methods of polarization imaging usually require the priori information of the background region, and rarely consider the effect of non-uniformity of the optical field on image recovery, which not only reduces the processing speed of imaging but also introduces errors in image recovery, especially for moving targets in complex scattering environments. In this paper, we propose a turbid underwater moving image recovery method based on the global estimation of the intensity and the degree of polarization (DOP) of the backscattered light, combined with polarization-relation histogram processing techniques. The full spatial distribution of the intensity and the DOP of the backscattered light are obtained by using frequency domain analysis and filtering. Besides, a threshold factor is set in the frequency domain low-pass filter, which is used to adjust the execution region of the filter, which effectively reduces the error in image recovery caused by estimating the DOP of the backscattered light as a constant in traditional methods with non-uniform illumination. Meanwhile, our method requires no human-computer interaction, which effectively solves the drawbacks that the moving target is difficult to be recovered by traditional methods. Experimental studies were conducted on static and moving targets under turbid water, and satisfactory image recovery quality is achieved.

Active non-uniform illumination-based underwater polarization imaging method for objects with complex polarization properties

Active polarization imaging is one of the most effective underwater optical imaging methods that can eliminate the degradation of image contrast and clarity caused by macro-molecule scattering. However, the non-uniformity of active illumination and the diversity of object polarization properties may decrease the quality of underwater imaging. This paper proposes a non-uniform illumination-based active polarization imaging method for underwater objects with complex optical properties. Firstly, illumination homogenization in the frequency domain is proposed to extract and homogenize the natural incident light from the total receiving light. Then, the weight values of the polarized and non-polarized images are computed according to each pixel's degree of linear polarization (DoLP) in the original underwater image. By this means, the two images can be fused to overcome the problem of reflected light loss generated by the complex polarization properties of underwater objects. Finally, the fusion image is normalized as the final result of the proposed underwater polarization imaging method. Both qualitative and quantitative experimental results show that the presented method can effectively eliminate the uneven brightness of the whole image and obtain the underwater fusion image with significantly improved contrast and clarity. In addition, the ablation experiment of different operation combinations shows that each component of the proposed method has noticeable enhancement effects on underwater polarization imaging. Our codes are available in Code 1.

Opto-electrical and polarization performance of a mesa-structured InGaAs PIN detector integrated with subwavelength aluminum gratings

Polarization detection in the short-wave infrared (SWIR) region presents broad applications in target-background contrast enhancement, underwater imaging, material classification, etc. A mesa structure can prevent electrical cross talk due to its intrinsic advantages, making it potentially suited to meet the need for manufacturing smaller-sized devices to save cost and shrink volume. In this Letter, mesa-structured InGaAs PIN detectors with a spectral response ranging from 900 nm to 1700 nm and a detectivity of 6.28 × 10¹¹cm·Hz^1/2/W at 1550 nm and -0.1 V bias (room temperature) have been demonstrated. Furthermore, the devices with subwavelength gratings in four orientations show obvious polarization performance. Their extinction ratios (ERs) can reach 18:1 at 1550 nm and their transmittances are over 90%. Such a polarized device with a mesa structure could realize miniaturized SWIR polarization detection.

Visibility enhancement of underwater images based on polarization common-mode rejection of a highly polarized target signal

Underwater active polarization imaging is promising due to its effect of significantly descattering. Polarization-difference is commonly used to filter out backscattered noise. However, the polarization common-mode rejection of target signal has rarely been utilized. In this paper, via taking full advantage of this feature of Stokes vectors S₂ which ably avoids interference from target light, the spatial variation of the degree of polarization of backscattered light is accurately estimated, and the whole scene intensity distribution of background is reconstructed by Gaussian surface fitting based on least square. Meanwhile, the underwater image quality measure is applied as optimization feedback, through iterative computations, not only sufficiently suppresses backscattered noise but also better highlights the details of the target. Experimental results demonstrate the effectiveness of the proposed method for highly polarized target in strongly scattering water.

Estimation and removal of backscattered light with nonuniform polarization information in underwater environments

The nonuniform of polarization information of backscattered light has always been a neglected characteristic in polarization underwater imaging, but its accurate estimation plays an important role in the quality of imaging results. Traditional polarization imaging methods assume that the degree of polarization and angle of polarization of backscattered light are constant. In fact, the polarization information of backscattering light is gradual, this assumption makes traditional methods work only in a small area of the camera's field of view, in which the change of the polarization information of backscattered light can be ignored. In this paper, by analyzing the distribution of backscattered light, it is concluded that its polarization information has the characteristics of low-rank. Then, the degree of polarization and angle of polarization of backscattered light were estimated by low-rank and sparse matrix decomposition, and the clear scene was reconstructed. Experimental results show that the proposed method breaks through the limitation of the assumption of backscattered light in traditional polarization imaging method, and expands the detection field under the same conditions, which makes it possible to develop polarization underwater imaging method to the direction of large field of view detection.

Physics-informed neural network for polarimetric underwater imaging

Utilizing the polarization analysis in underwater imaging can effectively suppress the scattered light and help to restore target signals in turbid water. Neural network-based solutions can also boost the performance of polarimetric underwater imaging, while most of the existing networks are pure data driven which suffer from ignoring the physical mode. In this paper, we proposed an effective solution that informed the polarimetric physical model and constrains into the well-designed deep neural network. Especially compared with the conventional underwater imaging model, we mathematically transformed the two polarization-dependent parameters to a single parameter, making it easier for the network to converge to a better level. In addition, a polarization perceptual loss is designed and applied to the network to make full use of polarization information on the feature level rather than on the pixel level. Accordingly, the network was able to learn the polarization modulated parameter and to obtain clear de-scattered images. The experimental results verified that the combination of polarization model and neural network was beneficial to improve the image quality and outperformed other existing methods, even in a high turbidity condition.

BM3D-based denoising method for color polarization filter array

Color split-focal plane polarization imaging systems are composed of image sensors with a color polarization filter array (CPFA). The noise generated during image acquisition leads to incorrect estimation of the color polarization information. Therefore, it is necessary to denoise CPFA image data. In this study, we propose a CPFA block-matching and 3D filtering (CPFA-BM3D) algorithm for CPFA image data. The algorithm makes full use of the correlation between different polarization channels and different color channels, restricts the grouping of similar 2D image blocks to form 3D blocks, and attenuates Gaussian noise in the transform domain. We evaluate the denoising performance of the proposed algorithm using simulated and real CPFA images. Experimental results show that the proposed method significantly suppresses noise while preserving the image details and polarization information. Its peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) indicators are superior to those of the other existing methods. The mean values of the PSNR and SSIM of the degree of linear polarization (DoLP) color images calculated through CPFA image interpolation can be increased to 200% and 400%, respectively, by denoising with the proposed method.

Underwater image restoration via Stokes decomposition

In this Letter, we present a Stokes imaging-based method to restore objects and enhance image contrast in turbid water. In the system, a light source illuminates the objects with two orthometric polarization states; based on a new Stokes decomposition model, the recorded images are converted to Stokes maps and subsequently restored to a clear image, free of reflections and scattered lights. A mathematical model has been developed to explain the Stokes decomposition and how the undesired reflections and scattered lights are rejected. Imaging experiments have been devised and performed on different objects, e.g., metals and plastics, under different turbidities. The results demonstrate enhanced image quality and capability to distinguish polarization differences. This new, to the best of our knowledge, method can be readily applied to practical underwater object detection and potentially realize clear vision in other scattering media.

Image-restoration algorithm based on an underwater polarization imaging visualization model

The polarization bidirectional reflection distribution function theory of a target is combined with microfacet theory, and the Monte Carlo method is used to establish an underwater laser active-polarization imaging model based on Mie scattering theory. The model presented herein can simulate imaging of an underwater target with a high degree of polarization, and the effects of optical thickness and target surface roughness on active underwater laser imaging results are demonstrated by the simulation image. Combined with histogram equalization and the traditional polarization differential imaging algorithm, an algorithm is presented herein that globally estimates the mutual information value between the target polarization degree and the correction factor of backscattered light polarization degree. The images received from the simulation test can be restored, and results show that the algorithm can restore the target image with a high degree of polarization to some extent. Finally, the correctness of the active underwater laser polarization imaging model and the feasibility of global estimation based on the polarization differential restoration algorithm are verified experimentally.

Time-of-Flight Imaging in Fog Using Polarization Phasor Imaging

Due to the light scattered by atmospheric aerosols, the amplitude image contrast is degraded and the depth measurement is greatly distorted for time-of-flight (ToF) imaging in fog. The problem limits ToF imaging to be applied in outdoor settings, such as autonomous driving. To improve the quality of the images captured by ToF cameras, we propose a polarization phasor imaging method for image recovery in foggy scenes. In this paper, optical polarimetric defogging is introduced into ToF phasor imaging, and the degree of polarization phasor is proposed to estimate the scattering component. A polarization phasor imaging model is established, aiming at separating the target component from the signal received by ToF cameras to recover the amplitude and depth information. The effectiveness of this method is confirmed by several experiments with artificial fog, and the experimental results demonstrate that the proposed method significantly improves the image quality, with robustness in different thicknesses of fog.

Deep Learning Based Filtering Algorithm for Noise Removal in Underwater Images

Under-water sensing and image processing play major roles in oceanic scientific studies. One of the related challenges is that the absorption and scattering of light in underwater settings degrades the quality of the imaging. The major drawbacks of underwater imaging are color distortion, low contrast, and loss of detail (especially edge information). The paper proposes a method to address these issues by de-noising and increasing the resolution of the image using a model network trained on similar data. The network extracts frames from a video and filters them with a trigonometric–Gaussian filter to eliminate the noise in the image. It then applies contrast limited adaptive histogram equalization (CLAHE) to improvise the image contrast, and finally enhances the image resolution. Experimental results show that the proposed method could effectively produce enhanced images from degraded underwater images.

Automatic underwater polarization imaging without background region or any prior

Previous polarization underwater imaging methods based on the physical scattering model usually require background region included in the image and the prior knowledge, which hinders its practical application. In this paper, we analyze and optimize the physically feasible region and propose an improved method by degenerating intermediate variables, which can realize automatic underwater image recovery without background region or any prior. The proposed method does not need to estimate the intermediate variables in the traditional underwater imaging model and is adaptable to the underwater image with non-uniform illumination, which avoids the poor and unstable image recovery performance caused by inaccurate estimation of intermediate parameters due to the improper identification of the background region. Meanwhile, our method is effective for both images without background region and images in which the background region is hard to be identified. In addition, our method solves the significant variation in recovery results caused by the different selection of background regions and the inconsistency of parameter adjustment. The experimental results of different underwater scenes show that the proposed method can enhance image contrast while preserving image details without introducing considerable noise, and the proposed method is effective for the dense turbid medium.

Underwater image recovery utilizing polarimetric imaging based on neural networks

Underwater imaging faces challenges due to complex optical properties in water. Our purpose is to explore the application of polarimetric imaging in image recovery under turbid water based on deep learning. A polarization camera is used to capture the polarization images of objects under water as datasets. The method used in our study aims to explore a structure and loss function that is suitable for the model. In terms of the model structure, four pairs of models consisting of polarized version and gray version based on the idea of dense U-Net and information flow were proposed. In the aspect of loss function, the method of combining weighted mean squared error with perceptual loss was proposed and a proper set of loss weights was selected through comparison experiments. Comparing the model outputs, it is found that adding polarized information along with the light intensity information to the model at the very front of the model structure brings about better recovering image. The model structure proposed can be used for image recovery in turbid water or other scattering environments. Since the polarization characteristics are considered, the recovered image has more detailed features than that where only intensity is considered. The results of comparison with other methods show the effectiveness of the proposed method.

Unsupervised learning polarimetric underwater image recovery under nonuniform optical fields

Turbid media will lead to a sharp decline in image quality. Polarization imaging is an effective method to obtain clear images in turbid media. In this paper, we propose an improved method that combines unsupervised learning and polarization imaging theory, which can be applied in a variety of nonuniform optical fields. We treat the background light as a spatially variable parameter, so we designed an end-to-end unsupervised generative network to inpaint the background light, which produces an adversarial loss with the discriminative network to improve the performance. And we use the angle of polarization to estimate the polarization parameters. The experimental results have demonstrated the effectiveness and generalization ability of our method. Compared with other works, our method shows a better real-time performance and has a lower cost in preparing the training dataset.

Low-pass filtering based polarimetric dehazing method for dense haze removal

Polarimetric dehazing method is very promising in enhancing the quality of images captured in the scattering media. However, it is found that the dehazing results calculated by hazy images are very sensitive to the noise, which may cause the method unstable or even invalid. To overcome this drawback and enhance the capability and stability of the polarimetric dehazing method, digital image processing algorithms or bias parameters need to be added into the method, however, they will make the algorithm complex and time consuming. In this paper, using low pass filter to suppress the noise of the hazy images, a novel polarimetric dehazing method is proposed to enhance the visibility of hazy images, especially for dense haze removal. Experimental results demonstrate that this method is totally automatic and very effective in dense haze processing. This method may have great potential usage in many applications, such as optical surveillance, underwater imaging, and bio-tissue imaging, etc.

Enhancement of underwater vision by fully exploiting the polarization information from the Stokes vector

Underwater imaging method based on polarization information is extremely popular due to its ability to effectively remove the backscattered light. The Stokes vector contains the information of both the degree and angle of polarization of the light wave. However, this aspect has been rarely utilized in image reconstruction. In this study, an underwater polarimetric imaging model is established by fully exploiting this feature of Stokes vectors. The transmission of light wave is described in terms of the polarization information derived from the Stokes vector. Then, an optimization function is designed based on the independent characteristics of target light and backscattered light to estimate the target and backscattered field information. The real-world experiments and mean squared error analysis verify that the proposed method can remove the backscattered light and recover the target information accurately.

Polarization prior to single-photon counting image denoising

Single-photon counting (SPC) imaging technique, which can detect targets in extremely low light levels, has attracted considerable research interest in recent years. To reduce the influence of noise under the low light condition, traditional approaches typically seek various priors from images themselves to construct denoising models, leading to inferior performance as the signal and noise cannot be efficiently distinguished. To address this challenging problem, in this study we propose a novel polarization prior to SPC image denoising based on the observation that a special polarization SPC (PSPC) image has a higher SNR than the SPC image. It enables us to construct a polarization prior to the PSPC image that can transfer efficient targets' spatial details to the denoised SPC image, and hence improves the denoising performance. Specifically, we group similar patches of the PSPC image to form 'anti-noise' dictionaries with high SNR. Then we construct a non-local prior-oriented sparse representation constraint based on the fact that each noisy patch of the SPC image can be sparsely represented by the corresponding 'anti-noise' dictionary. According to this sparse representation constraint, we further formulate an SPC image denoising model by incorporating two terms, i.e., a negative Poisson log-likelihood function for preserving the data fidelity and a total variation constraint to reduce the influence of noise, which is solved by an efficient variable splitting method. In the experiment, we have verified the effectiveness of the proposed method from simulated and real data in terms of visual comparison and quantitative analysis, respectively.

Fast processing of underwater polarization imaging based on optical correlation

Underwater polarization differential imaging requires the estimation of different parameters, and the parameters can be accurately obtained by using optical correlation. However, optical correlation as a criterion function to estimate parameters takes a lot of time. To expedite the parameters' estimation process, we propose two operations to process underwater polarization images. One operation is to update the analyzer angle range to reduce the number of processed images. The other is image downsampling, which reduces the amount of calculation for the corresponding images. In experiments, we confirmed the feasibility of our method. We have obtained an average of 42 times the calculation speed increase under the conditions of updating the analyzer angle range 3 times and reducing the image scale by 16 times. The results of our method are consistent with those of traditional methods. This established method is conducive to the practical application of underwater polarization differential imaging.

Performances of Polarization-Retrieve Imaging in Stratified Dispersion Media

We constructed an active imaging model within 10 km of the atmosphere from the satellite to the ground based on Monte Carlo (MC) algorithm, and, because of the inhomogeneous distributions of the scattering particles in atmosphere environment, 10 km atmosphere layer was divided into ten layers in our model. The MC algorithm was used to simulate the transmission process of photons through the atmosphere. By launching lasers of linear polarization states from satellites to ground, the intensity, degree of polarization (DoP), polarization difference (PD), and polarization retrieve (PR) images can be obtained. The contrast of the image, peak signal to noise ratio (PSNR), and structural similarity index (SSI) were used to evaluate the imaging quality. The simulated results demonstrate that the contrast of images is degraded as the atmosphere becomes worse. However, PR imaging have a better contrast and better visibility in different atmospheric conditions. Meanwhile, we found that Mueller matrix (MM) can retrieve the original images very well in a certain range of atmospheric conditions. Finally, the simulation also shows that different wavelengths of light sources have different penetration characteristics, and, in general, infrared light shows better performances than visible light for imaging.

Learning-based denoising for polarimetric images

Based on measuring the polarimetric parameters which contain specific physical information, polarimetric imaging has been widely applied to various fields. However, in practice, the noise during image acquisition could lead to the output of noisy polarimetric images. In this paper, we propose, for the first time to our knowledge, a learning-based method for polarimetric image denoising. This method is based on the residual dense network and can significantly suppress the noise in polarimetric images. The experimental results show that the proposed method has an evident performance on the noise suppression and outperforms other existing methods. Especially for the images of the degree of polarization and the angle of polarization, which are quite sensitive to the noise, the proposed learning-based method can well reconstruct the details flooded in strong noise.

Nonlinear reconstruction of weak optical diffused images under turbid water

Forward scattering noise may degrade the imaging resolution and diffuse the image in turbid water. The reconstruction of diffused images hidden by forward scattering noise is crucial for underwater imaging. To overcome the limitation of forward scattering for optical imaging in turbid water, a nonlinear image reconstruction technology is proposed in the experiment. We experimentally demonstrated the reconstruction of the diffused images under turbid water via signal seeded incoherent modulation instability (MI) in a nonlinear photorefractive crystal. The reconstructed image with high quality and the minimum resolution of 28.51 lp/mm are observed in the experiment. This is the first time, to the best of our knowledge, that a spatial MI effect is used to process underwater weak optical diffused images in the experiment.

Broadband ellipso-polarimetric camera utilizing tunable liquid crystal achromatic waveplate with improved field of view

An ellipso-polarimetric camera integrated with improved field of view tunable achromatic waveplate (AWP) over wide spectral band based on nematic liquid crystal retarders is presented. The AWP operates as half, quarter and full waveplate over a wide range of 430-780nm and wide field of view. The proposed analysis proved that capturing images at these modes is sufficient to extract the ellipsometric parameters: sin(2ψ), cos(Δ) and the Stokes parameters S₁ and S₃, besides showing the relations in between. Transmission and reflection modes setups are demonstrated in addition to an ellipso-polarimetric smartphone camera. The results show for the first time superiority of cos(Δ) images in which prominent contrast and fine details appear even with scattering objects and higher immunity to device errors. Biometric, remote sensing and archeological improved imaging applications are demonstrated.

An end-to-end fully-convolutional neural network for division of focal plane sensors to reconstruct S0, DoLP, and AoP

Division of focal plane (DoFP) polarimeter is widely used in polarization imaging sensors. The periodically arranged micro-polarizers integrated on the focal plane ensure its outstanding real-time performance, but reduce the spatial resolution of output images and further affect the calculation of polarization parameters. In this paper, a four-layer, end-to-end fully convolutional neural network called Fork-Net is proposed, which aims to directly improve the imaging quality of three polarization properties: intensity (i.e., S₀), degree of linear polarization (DoLP), and angle of polarization (AoP), rather than focusing on reducing the interpolation error of intensity images of different polarization orientations. The Fork-Net accepts raw mosaic images as input and directly outputs S₀, DoLP, and AoP. It is also trained with a customized loss function. The experimental results show that compared with existing methods, the proposed one achieves the highest peak signal-to-noise ratio (PSNR) and prominent visual quality on output images.

Polarization-based exploration for clear underwater vision in natural illumination

Underwater imaging provides human vision system friendly images; however, it often suffers from severe image degradation. This research developed an underwater polarization imaging model, which considers the water scattering effect, as well as absorption effect. It fully explored the polarization information of the target scene that backscattered light is partially polarized and target light is unpolarized. Then backscattered light is first estimated and removed. The target scene's distance information is derived based upon the polarization information, and then applied to develop a distance-based Lambertian model. This model enables estimation of the intensity loss caused by water absorption and accurate target radiance recovery. Furthermore, real-world experiments show that the developed model handled the underwater image degradation well. In particular, it enables effective color cast correction resulting from water absorption, which traditional imaging methods always ignore.

Polarimetric image recovery in turbid media employing circularly polarized light

Circular polarization memory is a well-known phenomenon indicating that the circular polarization light can persist better its polarization property during propagating through turbid media compared with the linear polarization light. Therefore, in principle, using circularly polarized light can probably improve the quality of image recovery in dense turbid media than using the linearly polarized light. In this paper, we propose a new polarimetric image recovery method in dense turbid media with the illumination light of circular polarization, and we realize the image recovery combining the circular polarization information and linearly polarization information. The real-world experiment results demonstrate that the proposed method is more effective than previous methods, including the traditional polarimetric image recovery method by Schechner's [Appl. Opt.42, 511 (2003)] based on linear polarization.

Polarimetric image recovery method combining histogram stretching for underwater imaging

The underwater imaging could be severely degraded by the scattering media because of the backscattered light and signal attenuation, especially in the case of strong scattering for dense turbid medium. In this paper, we propose an improved method for recovering the underwater image combining the histogram stretching and polarimetric recovery in a proper way. In this method, we stretch the histograms of the orthogonal polarization images while maintaining the polarization relation between them, and then, based on the processed orthogonal polarization images, the recovered image with higher quality can be obtained by the traditional polarimetric recovery method. Several groups of experimental results demonstrate that the quality of underwater images can be effectively enhanced by our method, and its performance is better than that of the traditional polarimetric recovery method. In particular, the proposed method is also quite effective in the condition of dense turbid medium.

Absolute Measurement of the Refractive Index of Water by a Mode-Locked Laser at 518 nm

In this paper, we demonstrate a method using a frequency comb, which can precisely measure the refractive index of water. We have developed a simple system, in which a Michelson interferometer is placed into a quartz-glass container with a low expansion coefficient, and for which compensation of the thermal expansion of the water container is not required. By scanning a mirror on a moving stage, a pair of cross-correlation patterns can be generated. We can obtain the length information via these cross-correlation patterns, with or without water in the container. The refractive index of water can be measured by the resulting lengths. Long-term experimental results show that our method can measure the refractive index of water with a high degree of accuracy—measurement uncertainty at 10⁻⁵ level has been achieved, compared with the values calculated by the empirical formula.

Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network

Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA) deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.

Feeble object detection of underwater images through LSR with delay loop

Feeble object detection is a long-standing problem in vision based underwater exploration work. However, because of the complicated light propagation situation and high background noise, underwater images are highly degraded. Noise is not always detrimental. Logical stochastic resonance (LSR) can be a useful tool for amplifying feeble signals by utilizing the constructive interplay of noise and a nonlinear system. In the present study, an appropriate LSR structure with a delay loop is proposed to process a low-quality underwater image for enhancing the vision detection accuracy of underwater feeble objects. Ocean experiments are conducted to demonstrate the effectiveness of the proposed structure. We also give explicit numerical results to illustrate the relationship between the structure of LSR and the correct detection probability. Methods presented in this paper are quite general and can thus be potentially extended to other applications for obtaining better performance.

Real-time image haze removal using an aperture-division polarimetric camera

Polarimetric dehazing methods have been proven to be effective in enhancing the quality of images acquired in turbid media. We report a new full-Stokes polarimetric camera, which is based on the division of aperture structure. We design a kind of automatic polarimetric dehazing algorithm and load it into the field programmable gate array (FPGA) modules of our designed polarimetric camera, achieving a real-time image haze removal with an output rate of 25 fps. We demonstrate that the image quality can be significantly improved together with a good color restoration. This technique might be attractive in a range of real-time outdoor imaging applications, such as navigation, monitoring, and remote sensing.