Automatic underwater polarization imaging without background region or any prior

Dual-polarization defogging method based on frequency division and blind separation of polarization information

The current advancements in image processing have led to significant progress in polarization defogging methods. However, most existing approaches are not suitable for scenes with targets exhibiting a high degree of polarization (DOP), as they rely on the assumption that the detected polarization information solely originates from the airlight. In this paper, a dual-polarization defogging method connecting frequency division and blind separation of polarization information is proposed. To extract the polarization component of direct transmission light from the detected polarized signal, blind separation of overlapped polarized information is performed in the low-frequency domain based on visual perception. Subsequently, after estimating airlight, a high-quality defogging image can be restored. Extensive experiments conducted on real-world scenes and comparative tests confirm the superior performance of our proposed method compared to other competitive methods, particularly in reconstructing objects with high DOP. This work provides a quantitative approach for estimating the contributions of polarization light from different sources and further expands the application range of polarimetric defogging imaging.

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.

Hierarchical deconvolution dehazing method based on transmission map segmentation

Images captured in fog are often affected by scattering. Due to the absorption and scattering of light by aerosols and water droplets, the image quality will be seriously degraded. The specific manifests are brightness decrease, contrast decrease, image blur, and noise increase. In the single-image dehazing method, the image degradation model is essential. In this paper, an effective image degradation model is proposed, in which the hierarchical deconvolution strategy based on transmission map segmentation can effectively improve the accuracy of image restoration. Specifically, the transmission map is obtained by using the dark channel prior (DCP) method, then the transmission histogram is fitted. The next step is to divide the image region according to the fitting results. Furthermore, to more accurately recover images of complex objects with a large depth of field, different levels of inverse convolution are adopted for different regions. Finally, the sub-images of different regions are fused to get the dehazing image. We tested the proposed method using synthetic fog images and natural fog images respectively. The proposed method is compared with eight advanced image dehazing methods on quantitative rating indexes such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image entropy, natural image quality evaluator (NIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). Both subjective and objective evaluations show that the proposed method achieves competitive results.

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 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.

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.

Haze optical-model-based nighttime image dehazing by modifying attenuation and atmospheric light

Scene imaging is often affected by artificial light sources within a hazy environment at night, causing degraded images with low brightness, color distortion, and glow. These problems render the traditional atmospheric scattering optical model obsolete and incompatible. To address this issue, we established an optical imaging model suitable for nighttime dehazing, and an illumination component is incorporated into the attenuation term. We also introduced the near-light source coefficient to redefine the glow. Based on this model, we propose a new nighttime dehazing method. First, the rough atmospheric light is estimated using its low-frequency characteristics. Then, the glow is calculated by the near-light source coefficient. Finally, we remove the haze and illumination to get a clear image. Extensive experiments prove that our method exhibits a better color recovery effect, which effectively improves the visibility and detail. Furthermore, we believe our method outperforms other methods, both qualitatively and quantitatively.

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.