Enhancing underwater optical imaging by using a low-pass polarization filter

Computational Polarization Imaging In Vivo through Surgical Smoke Using Refined Polarization Difference

In surgery, the surgical smoke generated during tissue dissection and hemostasis can degrade the image quality, affecting tissue visibility and interfering with the further image processing. Developing reliable and interpretable computational imaging methods for restoring smoke-affected surgical images is crucial, as typical image restoration methods relying on color-texture information are insufficient. Here a computational polarization imaging method through surgical smoke is demonstrated, including a refined polarization difference estimation based on the discrete electric field direction, and a corresponding prior-based estimation method, for better parameter estimation and image restoration performance. Results and analyses for ex vivo, the first in vivo animal experiments, and human oral cavity tests show that the proposed method achieves visibility restoration and color recovery of higher quality, and exhibits good generalization across diverse imaging scenarios with interpretability. The method is expected to enhance the precision, safety, and efficiency of advanced image-guided and robotic surgery.

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.

Underwater Turbid Media Stokes-Based Polarimetric Recovery

Underwater optical imaging for information acquisition has always been an innovative and crucial research direction. Unlike imaging in the air medium, the underwater optical environment is more intricate. From an optical perspective, natural factors such as turbulence and suspended particles in the water cause issues like light scattering and attenuation, leading to color distortion, loss of details, decreased contrast, and overall blurriness. These challenges significantly impact the acquisition of underwater image information, rendering subsequent algorithms reliant on such data unable to function properly. Therefore, this paper proposes a method for underwater image restoration using Stokes linearly polarized light, specifically tailored to the challenges of underwater complex optical imaging environments. This method effectively utilizes linear polarization information and designs a system that uses the information of the first few frames to calculate the enhanced images of the later frames. By doing so, it achieves real-time underwater Stokes linear polarized imaging while minimizing human interference during the imaging process. Furthermore, the paper provides a comprehensive analysis of the deficiencies observed during the testing of the method and proposes improvement perspectives, along with offering insights into potential future research directions.

Underwater dynamic polarization imaging without dependence on the background region

Active-polarization imaging holds significant promise for achieving clear underwater vision. However, only static targets were considered in previous studies, and a background region was required for image restoration. To address these issues, this study proposes an underwater dynamic polarization imaging method based on image pyramid decomposition and reconstruction. During the decomposition process, the polarized image is downsampled to generate an image pyramid. Subsequently, the spatial distribution of the polarization characteristics of the backscattered light is reconstructed by upsampling, which recovered the clear scene. The proposed method avoids dependence on the background region and is suitable for moving targets with varying polarization properties. The experimental results demonstrate effective elimination of backscattered light while sufficiently preserving the target details. In particular, for dynamic targets, processing times that fulfill practical requirements and yield superior recovery effects are simultaneously obtained.

Underwater single photon 3D imaging with millimeter depth accuracy and reduced blind range

Mono-static system benefits from its more flexible field of view and simplified structure, however, the backreflection photons from mono-static system lead to count loss for target detection. Counting loss engender range-blind, impeding the accurate acquisition of target depth. In this paper, count loss is reduced by introducing a polarization-based underwater mono-static single-photon imaging method, and hence reduced blind range. The proposed method exploits the polarization characteristic of light to effectively reduce the count loss of the target, thus improving the target detection efficiency. Experiments demonstrate that the target profile can be visually identified under our method, while the unpolarization system can not. Moreover, the ranging precision of system reaches millimeter-level. Finally, the target profile is reconstructed using non-local pixel correlations algorithm.

Computational imaging and occluded objects perception method based on polarization camera array

Traditional optical imaging relies on light intensity information from light reflected or transmitted by an object, while polarization imaging utilizes polarization information of light. Camera array imaging is a potent computational imaging technique that enables computational imaging at any depth. However, conventional imaging methods mainly focus on removing occlusions in the foreground and targeting, with limited attention to imaging and analyzing polarization characteristics at specific depths. Conventional camera arrays cannot be used for polarization layered computational imaging. Thus, to study polarization layered imaging at various depths, we devised a flexible polarization camera array system and proposed a depth-parallax relationship model to achieve computational imaging of polarization arrays and polarization information reconstruction under varying conditions and depths. A series of experiments were conducted under diverse occlusion environments. We analyzed the distinctive characteristics of the imaging results obtained from the polarization array, employing a range of array distribution methods, materials, occlusion density, and depths. Our research successfully achieved computational imaging that incorporates a layered perception of objects. Finally, we evaluated the object region's polarization information using the gray level co-occurrence matrix feature method.

Optimized method for polarization-based image dehazing

Image dehazing is desired under the foggy, rainy weather, or the underwater condition. Since the polarization-based image dehazing utilizes additional polarization information of light to de-scatter, image detail can be recovered well, but how to segment the polarization information of the background radiance and the object radiance becomes the key problem. For solving this problem, a method which combing polarization and contrast enhancement is demonstrated. This method contains two main steps, (a) by seeking the region of large mean intensity, low contrast and large mean degree of polarization, the no-object region can be found, and (b) through defining a weight function and judging whether the dehazed image can achieve high contrast and low information loss, the degree of polarization for object radiance can be estimated. Based on the estimated parameters, the scatter of light by the mediums can be diminished considerably. The theoretical derivation shows that this method can achieve advantages complementation, such as being able to obtain more details like the polarization-based method and high image contrast like the contrast enhancement based method. Besides, it is physically sound and can achieve good dehazing performance under different conditions, which has been verified by different hazing polarization images.

Increased range and contrast in fog with circularly polarized imaging

Fogs, low lying clouds, and other highly scattering environments pose a challenge for many commercial and national security sensing systems. Current autonomous systems rely on optical sensors for navigation whose performance is degraded by highly scattering environments. In our previous simulation work, we have shown that polarized light can penetrate through a scattering environment such as fog. We have demonstrated that circularly polarized light maintains its initial polarization state better than linearly polarized light, even through large numbers of scattering events and thus ranges. This has recently been experimentally verified by other researchers. In this work, we present the design, construction, and testing of active polarization imagers at short-wave infrared and visible wavelengths. We explore multiple polarimetric configurations for the imagers, focusing on linear and circular polarization states. The polarized imagers were tested at the Sandia National Laboratories Fog Chamber under realistic fog conditions. We show that active circular polarization imagers can increase range and contrast in fog better than linear polarization imagers. We show that when imaging typical road sign and safety retro-reflective films, circularly polarized imaging has enhanced contrast throughout most fog densities/ranges compared to linearly polarized imaging and can penetrate over 15 to 25 m into the fog beyond the range limit of linearly polarized imaging, with a strong dependence on the interaction of the polarization state with the target materials.

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.

Influence mechanism of the particle size on underwater active polarization imaging of reflective targets

Underwater active polarization imaging is a promising imaging method, however, it is ineffective in some scenarios. In this work, the influence of the particle size from isotropic (Rayleigh regime) to forward-scattering on polarization imaging is investigated by both Monte Carlo simulation and quantitative experiments. The results show the non-monotonic law of imaging contrast with the particle size of scatterers. Furthermore, through polarization-tracking program, the polarization evolution of backscattered light and target diffuse light are detailed quantitatively with Poincaré sphere. The findings indicate that the noise light's polarization and intensity scattering field change significantly with the particle size. Based on this, the influence mechanism of the particle size on underwater active polarization imaging of reflective targets is revealed for the first time. Moreover, the adapted principle of scatterer particle scale is also provided for different polarization imaging methods.

Intelligent Fusion Imaging Photonics for Real-Time Lighting Obstructions

Dynamic detection in challenging lighting environments is essential for advancing intelligent robots and autonomous vehicles. Traditional vision systems are prone to severe lighting conditions in which rapid increases or decreases in contrast or saturation obscures objects, resulting in a loss of visibility. By incorporating intelligent optimization of polarization into vision systems using the iNC (integrated nanoscopic correction), we introduce an intelligent real-time fusion algorithm to address challenging and changing lighting conditions. Through real-time iterative feedback, we rapidly select polarizations, which is difficult to achieve with traditional methods. Fusion images were also dynamically reconstructed using pixel-based weights calculated in the intelligent polarization selection process. We showed that fused images by intelligent polarization selection reduced the mean-square error by two orders of magnitude to uncover subtle features of occluded objects. Our intelligent real-time fusion algorithm also achieved two orders of magnitude increase in time performance without compromising image quality. We expect intelligent fusion imaging photonics to play increasingly vital roles in the fields of next generation intelligent robots and autonomous vehicles.

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.

Numerical simulation model of an optical filter using an optical vortex

Vortex beam has the potential to significantly improve the performance of lidar (light detection and ranging) and optical communication applications in which low signal-to-noise ratio (SNR) limits the detection/transmission range. The vortex beam method allows for spatially separating the coherent light (laser signal) from the incoherent light (the background radiation and multiple-scattered light) of the received signal. This paper presents results of a simulation model in which the optical vortex acts as an optical filter. We present instrument parameters that describe the filtering effect, e.g., the form of the vortex phase modulation function, the topological charge of the vortex and the focal length of a virtual Fresnel lens that is used for optical filtering. Preliminary experimental results show that the background radiation within the spectral filter bandwidth can be suppressed by as much as 95%. At the same time, we retain 97% of the coherent laser signal. Our simulation model will be used in future design of lidar instruments and optical communication systems in which the optical vortex method is used for optical filtering of the detected signals.

Fusion-based underwater image enhancement with category-specific color correction and dehazing

Underwater imaging is usually affected by water scattering and absorption, resulting in image blur and color distortion. In order to achieve color correction and dehazing for different underwater scenes, in this paper we report a fusion-based underwater image enhancement technique. First, statistics of the hue channel of underwater images are used to divide the underwater images into two categories: color-distorted images and non-distorted images. Then, category-specific combinations of color compensation and color constancy algorithms are used to remove the color shift. Second, a ground-dehazing algorithm using haze-line prior is employed to remove the haze in the underwater image. Finally, a channel-wise fusion method based on the CIE L* a* b* color space is used to fuse the color-corrected image and dehazed image. For experimental validation, we built a setup to acquire underwater images. The experimental results validate that the category-specific color correction strategy is robust to different categories of underwater images and the fusion strategy simultaneously removes haze and corrects color casts. The quantitative metrics on the UIEBD and EUVP datasets validate its state-of-the-art performance.

Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning

Underwater ghost imaging based on deep learning can effectively reduce the influence of forward scattering and back scattering of water. With the help of data-driven methods, high-quality results can be reconstructed. However, the training of the underwater ghost imaging requires enormous paired underwater datasets, which are difficult to obtain directly. Although the Cycle-GAN method solves the problem to some extent, the blurring degree of the fuzzy class of the paired underwater datasets generated by Cycle-GAN is relatively unitary. To solve this problem, a few-shot underwater image generative network method is proposed. Utilizing the proposed few-shot learning image generative method, the generated paired underwater datasets are better than those obtained by the Cycle-GAN method, especially under the condition of few real underwater datasets. In addition, to reconstruct high-quality results, an underwater deblurring ghost imaging method is proposed. The reconstruction method consists of two parts: reconstruction and deblurring. The experimental and simulation results show that the proposed reconstruction method has better performance in deblurring at a low sampling rate, compared with existing underwater ghost imaging methods based on deep learning. The proposed reconstruction method can effectively increase the clarity degree of the underwater reconstruction target at a low sampling rate and promotes the further applications of underwater ghost imaging.

Method for improving the measurement accuracy of binocular stereo vision in a scattering environment

In the scattering environment, binocular stereo vision measurement technology produces large errors due to the change of refractive index of the imaging light path and the decrease in target image contrast. To address this problem, this paper proposes a method for improving the measurement accuracy of binocular stereo vision in a scattering environment combined with polarization imaging theory. First, scattering images with different polarization directions are obtained and filtered by a Gaussian low-pass filter to calculate the degree of polarization and angle of polarization. Then, the scattered light intensity is calculated by using polarization information to obtain images after removing the scattering. Second, feature extraction and matching are carried out for the images after scattering removal. Finally, the target is measured based on the binocular stereo vision measurement model. The experimental results show that when the scattering concentration is high enough, the conventional method can no longer perform measurement, but the method proposed in this paper can still obtain the target parameters at this time, and can also improve measurement accuracy by at least 46.30%. In conclusion, the proposed method provides a valuable reference to improve the accuracy of binocular stereo vision measurement in a scattering environment by reducing the interference of scattering light.

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.

A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas

Based on analysis of state-of-the-art research investigating target detection and recognition in turbid waters, and aiming to solve the problems encountered during target detection and the unique influences of turbidity areas, in this review, the main problem is divided into two areas: image degradation caused by the unique conditions of turbid water, and target recognition. Existing target recognition methods are divided into three modules: target detection based on deep learning methods, underwater image restoration and enhancement approaches, and underwater image processing methods based on polarization imaging technology and scattering. The relevant research results are analyzed in detail, and methods regarding image processing, target detection, and recognition in turbid water, and relevant datasets are summarized. The main scenarios in which underwater target detection and recognition technology are applied are listed, and the key problems that exist in the current technology are identified. Solutions and development directions are discussed. This work provides a reference for engineering tasks in underwater turbid areas and an outlook on the development of underwater intelligent sensing technology in the future.

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.

Experimental Study on Bottom-Up Detection of Underwater Targets Based on Polarization Imaging

Previous studies on the polarization imaging of underwater targets mainly focused on top-down detection; however, the capacities of bottom-up detection were poorly known. Based on in situ experiments, the capability of bottom-up detection of underwater targets using polarization imaging was investigated. First, to realize the objective of bottom-up polarization imaging, a SALSA polarization camera was integrated into our Underwater Polarization Imaging System (UPIS), which was integrated with an attitude sensor. At Qiandao Lake, where the water is relatively clear, experiments were conducted to examine the capacity of the UPIS to detect objects from the bottom up. Simultaneously, entropy, clarity, and contrast were adopted to compare the imaging performance with different radiation parameters. The results show that among all the used imaging parameters, the angle of polarization is the optimal parameter for bottom-up detection of underwater targets based on polarization imaging, which may result from the different diffused reflectance of the target surface to the linear polarization components of the Stokes vector.

Polarization-based smoke removal method for surgical images

Smoke generated during surgery affects tissue visibility and degrades image quality, affecting surgical decisions and limiting further image processing and analysis. Polarization is a fundamental property of light and polarization-resolved imaging has been studied and applied to general visibility restoration scenarios such as for smog or mist removal or in underwater environments. However, there is no related research or application for surgical smoke removal. Due to differences between surgical smoke and general haze scenarios, we propose an alternative imaging degradation model by redefining the form of the transmission parameters. The analysis of the propagation of polarized light interacting with the mixed medium of smoke and tissue is proposed to realize polarization-based smoke removal (visibility restoration). Theoretical analysis and observation of experimental data shows that the cross-polarized channel data generated by multiple scattering is less affected by smoke compared to the co-polarized channel. The polarization difference calculation for different color channels can estimate the model transmission parameters and reconstruct the image with restored visibility. Qualitative and quantitative comparison with alternative methods show that the polarization-based image smoke-removal method can effectively reduce the degradation of biomedical images caused by surgical smoke and partially restore the original degree of polarization of the samples.

Recovery for underwater image degradation with multi-stage progressive enhancement

Optical absorption and scattering result in quality degradation of underwater images, which hampers the performance of underwater vision tasks. In practice, a well-posed underwater image recovery requires a combination of scene specificity and adaptability. To this end, this paper breaks down the overall recovery process into in-situ enhancement and data-driven correction modules, and proposes a Multi-stage Underwater Image Enhancement (MUIE) method to cascade the modules. In the in-situ enhancement module, a channel compensation with scene-relevant supervision is designed to address different degrees of unbalanced attenuation, and then the duality-based computation inverts the result of running a enhancement on inverted intensities to recover the degraded textures. In response to different scenarios, a data-driven correction, encoding corrected color-constancy information under data supervision, is performed to correct the improper color appearance of in-situ enhanced results. Further, under the collaboration between scene and data information, the recovery of MUIE avoids ill-posed response and reduces the prior dependence of specific scenes, resulting in a robust performance in different underwater scenes. Recovery comparison results confirm that the recovery of MUIE shows the superiority of scene clarity, realistic color appearance and evaluation scores. With the recovery of MUIE, the Underwater Image Quality Measurement (UIQM) scores of recovery-challenging images in the UIEB dataset were improved from 1.59 to 3.92.

Underwater Image Restoration via DCP and Yin–Yang Pair Optimization

Underwater image restoration is a challenging problem because light is attenuated by absorption and scattering in water, which can degrade the underwater image. To restore the underwater image and improve its contrast and color saturation, a novel algorithm based on the underwater dark channel prior is proposed in this paper. First of all, in order to reconstruct the transmission maps of the underwater image, the transmission maps of the blue and green channels are optimized by the proposed first-order and second-order total variational regularization. Then, an adaptive model is proposed to improve the first-order and second-order total variation. Finally, to solve the problem of the excessive attenuation of the red channel, the transmission map of the red channel is compensated by Yin–Yang pair optimization. The simulation results show that the proposed restored algorithm outperforms other approaches in terms of the visual effects, average gradient, spatial frequency, percentage of saturated pixels, underwater color image quality evaluation and evaluation metric.

A polarization-based image restoration method for both haze and underwater scattering environment

Existing polarization-based defogging algorithms rely on the polarization degree or polarization angle and are not effective enough in scenes with little polarized light. In this article, a method of image restoration for both haze and underwater scattering environment is proposed. It bases on the general assumption that gray variance and average gradient of a clear image are larger than those of an image in a scattering medium. Firstly, based on the assumption, polarimetric images with the maximum variance (Ibest) and minimum variance (Iworst) are calculated from the captured four polarization images. Secondly, the transmittance is estimated and used to remove the scattering light from background medium of Ibest and Iworst. Thirdly, two images are fused to form a clear image and the color is also restored. Experimental results show that the proposed method obtains clear restored images both in haze and underwater scattering media. Because it does not rely on the polarization degree or polarization angle, it is more universal and suitable for scenes with little polarized light.

Measurement error model of the bio-inspired polarization imaging orientation sensor

This article studies the measurement error model and calibration method of the bio-inspired polarization imaging orientation sensor (BPIOS), which has important engineering significance for promoting bio-inspired polarization navigation. Firstly, we systematically analyzed the measurement errors in the imaging process of polarized skylight and accurately established an error model of BPIOS based on Stokes vector. Secondly, using the simulated Rayleigh skylight as the incident surface light source, the influence of multi-source factors on the measurement accuracy of BPIOS is quantitatively given for the first time. These simulation results can guide the later calibration of BPIOS. We then proposed a calibration method of BPIOS based on geometric parameters and the Mueller matrix of the optical system and conducted an indoor calibration experiment. Experimental results show that the measurement accuracy of the calibrated BPIOS can reach 0.136°. Finally, the outdoor performance of BPIOS is studied. Outdoor dynamic performance test and field compensation were performed. Outdoor results show that the heading accuracy of BPIOS is 0.667°.

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.

Effective solution for underwater image enhancement

Degradation of underwater images severely limits people to exploring and understanding underwater world, which has become a fundamental but vital issue needing to be addressed in underwater optics. In this paper, we develop an effective solution for underwater image enhancement. We first employ an adaptive-adjusted artificial multi-exposure fusion (A-AMEF) and a parameter adaptive-adjusted local color correction (PAL-CC) to generate a contrast-enhanced version and a color-corrected version from the input respectively. Then we put the contrast enhanced version into the famous guided filter to generate a smooth base-layer and a detail-information containing detail-layer. After that, we utilize the color channel transfer operation to transfer color information from the color-corrected version to the base-layer. Finally, the color-corrected base-layer and the detail-layer are added together simply to reconstruct the final enhanced output. Enhanced results obtained from the proposed solution performs better in visual quality, than those dehazed by some current techniques through our comprehensive validation both in quantitative and qualitative evaluations. In addition, this solution can be also utilized for dehazing fogged images or improving accuracy of other optical applications such as image segmentation and local feature points matching.

Underwater image restoration via feature priors to estimate background light and optimized transmission map

Underwater images frequently suffer from color casts and poor contrast, due to the absorption and scattering of light in water medium. To address these two degradation issues, we propose an underwater image restoration method based on feature priors inspired by underwater scene prior. Concretely, we first develop a robust model to estimate the background light according to feature priors of flatness, hue, and brightness, which can effectively relieve color distortion. Next, we compensate the red channel of color corrected image to revise the transmission map of it. Coupled with the structure-guided filter, the coarse transmission map is refined. The refined transmission map preserves the edge information while improving the contrast. Extensive experiments on diverse degradation scenes demonstrate that our method achieves superior performance against several state-of-the-art methods.

Underwater image restoration by structured light and flood light imaging

An underwater optical imaging system is indispensable for many oceanic engineering tasks, yet still plagued by poor visibility conditions. The serious degradation of underwater image results from light scattering and absorption. Removal of the backscattered light is the focus issue of underwater imaging technology to improve the image visibility, particularly in turbid water. In this paper, we present an approach for underwater image recovery using structured light imaging and flood light imaging to compose a combined imaging model with which the backscatter component is completely offset. The convolutional image is obtained using the structured light scanning imaging mode where the backscatter intensity is proportional to that of the flood light image with an unknown scale parameter. An algorithm to refine the matching of the backscatter components of both the convolutional image and the flood light image is proposed. Thus, subtraction of both images gives rise the combined imaging model without the backscatter component. Consequently, image restoration is completed by employing the deconvolution process. Results of underwater experiments are given.

Underwater Image Restoration via Non-Convex Non-Smooth Variation and Thermal Exchange Optimization

The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange optimization is proposed. Firstly, the underwater dark channel prior is used to estimate the rough transmission map. Secondly, the rough transmission map is refined by the proposed adaptive non-convex non-smooth variation. Then, Thermal Exchange Optimization is applied to compensate for the red channel of underwater images. Finally, the restored image can be estimated via the image formation model. The results show that the proposed algorithm can output high-quality images, according to qualitative and quantitative analysis.

Imaging in turbid water based on a Hadamard single-pixel imaging system

Underwater imaging is a challenging task because of the large amounts of noise and the scattering nature of water. Conventional optical methods cannot realize clear imaging in underwater conditions owing to the limitations of low sensitivity, geometrical aberrations, and the narrow spectrum of photoelectric detectors. By contrast, single-pixel imaging (SPI) is a promising tool for imaging in poor-visibility environments. Nevertheless, this challenge is faced even when using traditional SPI methods in highly turbid underwater environments. In this work, we propose a Hadamard single-pixel imaging (HSI) system that outperforms other imaging modes in turbid water imaging. The effects of laser power, projection rate, and water turbidity on the final image quality are systematically investigated. Results reveal that compared with the state-of-the-art SPI techniques, the proposed HSI system is more promising for underwater imaging because of its high resolution and anti-scattering capabilities.

Visibility Restoration: A Systematic Review and Meta-Analysis

Image acquisition is a complex process that is affected by a wide variety of internal and environmental factors. Hence, visibility restoration is crucial for many high-level applications in photography and computer vision. This paper provides a systematic review and meta-analysis of visibility restoration algorithms with a focus on those that are pertinent to poor weather conditions. This paper starts with an introduction to optical image formation and then provides a comprehensive description of existing algorithms as well as a comparative evaluation. Subsequently, there is a thorough discussion on current difficulties that are worthy of a scientific effort. Moreover, this paper proposes a general framework for visibility restoration in hazy weather conditions while using haze-relevant features and maximum likelihood estimates. Finally, a discussion on the findings and future developments concludes this paper.

Underwater Object Detection and Reconstruction Based on Active Single-Pixel Imaging and Super-Resolution Convolutional Neural Network

Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this paper, we propose a single-pixel object inspection system for the underwater environment based on compressive sensing super-resolution convolutional neural network (CS-SRCNN). With the CS-SRCNN algorithm, image reconstruction can be achieved with 30% of the total pixels in the image. We also investigate the impact of compression ratios on underwater object SPI reconstruction performance. In addition, we analyzed the effect of peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to determine the image quality of the reconstructed image. Our work is compared to the SPI system and SRCNN method to demonstrate its efficiency in capturing object results from an underwater environment. The PSNR and SSIM of the proposed method have increased to 35.44% and 73.07%, respectively. This work provides new insight into SPI applications and creates a better alternative for underwater optical object imaging to achieve good quality.

Efficient Sky Dehazing by Atmospheric Light Fusion

In this article, we present a new method of dehazing based on the Koschmieder model, which aims to restore an image that has been affected by haze. The difficulty is to improve the estimation of the transmission and the atmospheric light that generally suffer from the nonhomogeneity and the random variability of the environment. The keypoint is to enhance the dehazing of very bright regions of the image in order to improve the treatment of the sky that is often overestimated or underestimated compared to the rest of the scene. The approach proposed in this paper is based on two main contributions: 1. an L0 gradient optimization function weighted by a set of Gaussian filters and based on an iterative algorithm for optimization convergence. Unlike the existing methods using a single value of the atmospheric light for the whole image, our method uses a set of values neighboring an initial estimated value. The fusion is then applied based on Laplacian and Gaussian pyramids to combine all the relevant information from the set of images constructed from atmospheric lights and improves the contrast to recover the colors of the sky without any artifacts. Finally, the results are validated by three criteria: an autocorrelation score (ZNCC), a similarity measure (SSIM) and a visual criterion. The experiments carried out on two datasets show that our approach allows a better dehazing of the images with higher SSIM and ZNCC measurements but also with better visual quality.

Optical 4D signal detection in turbid water by multi-dimensional integral imaging using spatially distributed and temporally encoded multiple light sources

We propose an underwater optical signal detection system based on multi-dimensional integral imaging with spatially distributed multiple light sources and four-dimensional (4D) spatial-temporal correlation. We demonstrate our system for the detection of optical signals in turbid water. A 4D optical signal is generated from a three-dimensional (3D) spatial distribution of underwater light sources, which are temporally encoded using spread spectrum techniques. The optical signals are captured by an array of cameras, and 3D integral imaging reconstruction is performed, followed by multi-dimensional correlation to detect the optical signal. Inclusion of multiple light sources located at different depths allows for successful signal detection at turbidity levels not feasible using only a single light source. We consider the proposed system under varied turbidity levels using both Pseudorandom and Gold Codes for temporal signal coding. We also compare the effectiveness of the proposed underwater optical signal detection system to a similar system using only a single light source and compare between conventional and integral imaging-based signal detection. The underwater signal detection capabilities are measured through performance-based metrics such as receiver operating characteristic (ROC) curves, the area under the curve (AUC), and the number of detection errors. Furthermore, statistical analysis, including Kullback-Leibler divergence and Bhattacharya distance, shows improved performance of the proposed multi-source integral imaging underwater system. The proposed integral-imaging based approach is shown to significantly outperform conventional imaging-based methods.

Computer-vision-based intelligent adaptive transmission for optical wireless communication

Optical wireless communication (OWC) has been presented as a promising candidate for future space-air-ground-ocean-integrated communication. However, the OWC is quite sensitive to the variation of the channel transmission characteristics. The light beam absorption and the scattering in the transmission media affect not only the channel feature, but also the imaging quality. Thus, there is an inherent relationship between the OWC performance and the optical imaging quality. Based on this consideration, we firstly present the idea of introducing computer vision mechanisms into the OWC systems, and then propose a computer vision-based multi-domain cooperative adjustment (CV-MDCA) mechanism's functional modules to realize the intelligent adaptive transmission in OWC systems. The CV-MDCA mechanism are specifically designed, with the emphasis on how to quantitatively determine the exact on-line channel quality from the captured images by using effective computer vision schemes. Two groups of experiments, the indoor-simulated underwater visible light communication and the outdoor-practical atmospheric free-space optics, are implemented in order to evaluate the presented CV-MDCA mechanism's performance. The results not only validate the feasibility to determine the channel quality, according to the captured channel images, but also reveal the presented three computer vision-based criteria's limitations.