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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. [ABSTRACT FROM AUTHOR]

This paper focuses on the experimental characterization of the polarization behavior of light backscattered through fog. A polarimetric orthogonal state contrast imager and an active, purely polarized white illuminator system are used to evaluate both linear and circular polarization signals. The experiments are carried out in a macro-scale fog chamber under controlled artificial fog conditions. We explore the effect of backscattering in each imaging channel, and the persistence of both polarization signals as a function of meteorological visibility. We confirm the presence of the polarization memory effect with circularly polarized light, and, as a consequence, the maintenance of helicity in backscattering. Moreover, the circular cross-polarized channel is found to be the imaging channel less affected by fog backscattering. These results are useful and should be taken into account when considering active polarimetric imaging techniques for outdoor applications under foggy conditions. [ABSTRACT FROM AUTHOR]

Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the increasing amount of data and the rapid development of physical models, deep learning (DL) and its related technique have become an irreplaceable solution for solving various tasks and breaking the limitations of traditional methods. PI and DL have been combined successfully to provide brand-new solutions to many practical applications. This review briefly introduces PI and DL's most relevant concepts and models. It then shows how DL has been applied for PI tasks, including image restoration, object detection, image fusion, scene classification, and resolution improvement. The review covers the state-of-the-art works combining PI with DL algorithms and recommends some potential future research directions. We hope that the present work will be helpful for researchers in the fields of both optical imaging and RS, and that it will stimulate more ideas in this exciting research field. [ABSTRACT FROM AUTHOR]

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.

This paper focuses on the experimental characterization of the polarization behavior of light backscattered through fog. A polarimetric orthogonal state contrast imager and an active, purely polarized white illuminator system are used to evaluate both linear and circular polarization signals. The experiments are carried out in a macro-scale fog chamber under controlled artificial fog conditions. We explore the effect of backscattering in each imaging channel, and the persistence of both polarization signals as a function of meteorological visibility. We confirm the presence of the polarization memory effect with circularly polarized light, and, as a consequence, the maintenance of helicity in backscattering. Moreover, the circular cross-polarized channel is found to be the imaging channel less affected by fog backscattering. These results are useful and should be taken into account when considering active polarimetric imaging techniques for outdoor applications under foggy conditions.

Underwater images always suffer from low contrast and color distortion due to the wavelength-dependent scattering and absorption effects caused by particles existing in turbid water, especially in high turbidity conditions. Based on the polarization properties of the backscattering light, polarimetric methods can estimate the intensity level of the backscattering and the transmittance of the media. Accordingly, they can separate the target signal from the undesired ones to achieve high-quality imaging. In addition, learning-based polarimetric methods are effective for gray-model image restoration, but the learning-based polarimetric technique for color image restoration has yet to be considered. In this paper, we propose a 3- dimensional convolutional neural network, which maintains the correlation of polarization information among different polarization channel images as well as embodies polarization constraints, for underwater color image restoration. The experimental results verify that the proposed solution improves the image quality (i.e., the image contrast, details, and color) and outperforms other existing methods, especially when the turbidity of scattering media is high. The proposed solution can be readily applied to practical applications and potentially realize the clear vision in other scattering media, including biomedical imaging and remote sensing.

Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the increasing amount of data and the rapid development of physical models, deep learning (DL) and its related technique have become an irreplaceable solution for solving various tasks and breaking the limitations of traditional methods. PI and DL have been combined successfully to provide brand-new solutions to many practical applications. This review briefly introduces PI and DL’s most relevant concepts and models. It then shows how DL has been applied for PI tasks, including image restoration, object detection, image fusion, scene classification, and resolution improvement. The review covers the state-of-the-art works combining PI with DL algorithms and recommends some potential future research directions. We hope that the present work will be helpful for researchers in the fields of both optical imaging and RS, and that it will stimulate more ideas in this exciting research field.

Imaging in scattering media has been a challenging and important subject in optical science. In scattering media, the image quality is often severely degraded by the scattering and absorption effects owing to the small particles and the resulting nonuniform distribution of the intensity or polarization properties. This study reviews the recent development in polarimetric imaging techniques that address these challenges. Specifically, based on the polarization properties of the backscattering light, polarimetric methods can estimate the intensity level of the backscattering and the transmittance of the media. They can also separate the target signal from the undesired ones to achieve high-quality imaging. In addition, the different designs of the polarimetric imaging systems offer additional metrics, for example, the degree/angle of polarization, to recover images with high fidelity. We first introduce the physical degradation models in scattering media. Secondly, we apply the models in different polarimetric imaging systems, such as polarization difference, Stokes vector, Mueller matrix, and deep learning-based systems. Lastly, we provide a model selection guideline and future research directions in polarimetric imaging.

The scattering and absorption by the existing particles in turbid media is severely degrading the image quality and decreasing the visibility distance, especially in dense turbid media. In this paper, we investigate the polarization property of hazy images in dense turbid media, and we propose a simple pseudo-polarimetric dehazing method for dense haze removal, which only requires a single image captured by the detector. By our proposed method, two orthogonal polarization subimages are derived from the single original image, and then, based on these two subimages, one can obtain the recovered image by using the traditional polarimetric method. The real-word experimental results verify that, for the dense haze, our method based on a single image could have a comparable or better performance compared to the traditional polarimetric dehazing method with multiple images captured at different polarization states.

Collected sources for the different types of visualizations used in the field of polarization imaging are not extensive. Here, we survey and review the different visualization techniques in passive polarimetric imaging. Analysis of the methods is done by applying various concepts from the field of visualization. We provide recommendations for choosing a visualization based on the data structure, spatial frequency, and analysis goals. [ABSTRACT FROM AUTHOR]

The scattering and absorption by the existing particles in turbid media is severely degrading the image quality and decreasing the visibility distance, especially in dense turbid media. In this paper, we investigate the polarization property of hazy images in dense turbid media, and we propose a simple pseudo-polarimetric dehazing method for dense haze removal, which only requires a single image captured by the detector. By our proposed method, two orthogonal polarization subimages are derived from the single original image, and then, based on these two subimages, one can obtain the recovered image by using the traditional polarimetric method. The real-word experimental results verify that, for the dense haze, our method based on a single image could have a comparable or better performance compared to the traditional polarimetric dehazing method with multiple images captured at different polarization states. [ABSTRACT FROM AUTHOR]

Utilizing Stokes-correlometry analysis a new diagnostic approach has been introduced for quantitative assessment of polarization images of histological sections of optically anisotropic biological tissues with different morphological structures and physiological conditions. The developed approach is based on the quantitative assessment of coordinate and phase distributions of the Stokes vector of scattered light. A combined use of statistic, correlation, and fractal analysis is used for resolving variations in optical anisotropy of biological samples. The proposed combined application of the statistical, correlation, and fractal-based evaluates of spatial distributions of ‘single-point’ polarization azimuth, ellipticity, and ‘two-point’ Stokes vector parameters of polarization images of biological tissues histological sections demonstrates a high accuracy ($Ac \geq \text{90}\% $) in monitoring of optical anisotropy variations within biological tissues. [ABSTRACT FROM AUTHOR]

Although deep learning-based methods have achieved great success in various polarimetric imaging tasks, the performance and the generalization ability are strongly dependent on massive training data, which is a critical limitation and limits the practical applications. In this paper, for the first time to our knowledge, we present a deep transfer learning-based solution for polarimetric image denoising. This solution performs the transfer learning by fine-tuning a denoising model pre-trained on a large-scale color image dataset and using a small-scale polarimetric dataset. The experimental results show that, based on a small-scale dataset, the proposed network can achieve almost the same denoising performance as that with a large-scale dataset. The polarization parameters, i.e., the degree of polarization and the angle of polarization, can be reconstructed simultaneously. In addition, serials of experiments demonstrate the generalization ability of the method for different materials and noise levels. • Transfer learning method for polarimetric image denoising is proposed. • With a small-scale dataset, it achieves the same performance as a large-scale one. • The method has the generalization ability for different materials and noise levels. [ABSTRACT FROM AUTHOR]

A demand for division of focal plane (DoFP) polarization image sensors grows rapidly as nanofabrication technologies become mature. The DoFP sensor can output real time data of polarization information. In this paper, a novel visualization method for angle of polarization (AoP) is proposed for DoFP polarization image sensors. The data characteristics of AoP are analyzed, and strategies for a visualization method are proposed which conforms to the characteristics of AoP data. According to these strategies, we propose a visualization method for AoP data based on three dimensional HSI color space. This method uses intensity and saturation to characterize the magnitude of the angle between the polarization direction and the horizontal direction wherein the hue indicates the deflection direction. It is shown by the numerical simulation that the noise in the AoP image can be suppressed by our visualization method. In addition, the real-world experiment results are consistent with the numerical simulation and verify that the AoP image obtained by our method can suppress the influence of characterization noise, and the image is simple and intuitive, which is advantageous to human vision. The proposed method can be directly used for the commercialized DoFP polarization image sensor to display real-time AoP data.

• This article proposes an unsupervised polarimetric generative adversarial network for underwater-image recovery. • It breaks the dependence on strictly paired images and enhances recovery performance by informing polarization. • Experiments on different objects and imaging conditions verify the effectiveness and superiority of the method. Polarimetric imaging has prominent advantages in scattering media. Emerging learning-based polarimetric technologies has succeeded but heavily relied on paired data corresponding to the same scenes. In this paper, we propose an unsupervised method for the unpaired underwater-image recovery with a polarimetric generative adversarial network, named U 2 R-pGAN. The method breaks the dependence on strictly paired images in traditional learning-based methods and significantly enhances the restoration performance. Besides, we merge polarization losses into the network, which has been verified beneficial for details restoration. Imaging experiments have been devised and performed on different objects and viewing conditions under varying turbidity. The results demonstrate that the proposed method improves the peak signal to noise ratio by an average of 3.4 dB. The new method can be readily applied to practical underwater applications. [ABSTRACT FROM AUTHOR]

A promising method to optimize the polarization state of two-channel active polarization imaging system is presented. In this method, it is seminal that the detecting function of the imaging system is regarded as a discriminant projection of the observed objects’ polarization features (elements of the Mueller matrix). The polarization state can be seen as a physical classifier which can be obtained by training samples. The image acquired with the system that has the designed optimal polarization state become discriminative results directly. The effectiveness of the proposed method and the discriminative ability of the optimal polarization state are demonstrated by the experimental results. [ABSTRACT FROM AUTHOR]

A technique for creating maps of the direction and strength of fiber alignment in collagenous soft tissues is presented. The method uses a division of focal plane polarimeter to measure circularly polarized light transmitted through the tissue. The architecture of the sensor allows measurement of the retardance and fiber alignment at the full frame rate of the sensor without any moving optics. The technique compares favorably to the standard method of using a rotating polarizer. How the new technique enables real-time capture of the full angular spread of fiber alignment and retardance under various cyclic loading conditions is illustrated. [ABSTRACT FROM AUTHOR]

• This article proposes, for the first time to our knowledge, a learning-based method for underwater polarimetric image recovery. • The comparison experiment with the intensity or polarization information input demonstrates that additionally introducing the polarization information is beneficial for improving the image quality. • The experimental results for different scenes in dense turbid water demonstrate that our learning-based method can effectively remove the veiling light to realize the clear vision and outperforms other existing methods. Polarimetric imaging is an effective way for clear vision in water, in which deducing the object radiance in clear water from the obtained polarimetric information in turbid water is essential. In this letter, we propose, for the first time to our knowledge, a learning-based method for polarimetric underwater image recovery. It is based on the dense network and can learn well the relation between the object radiance and the polarization information. The experimental results demonstrate that additionally introducing the polarization information is beneficial for improving the image quality. Moreover, the proposed learning-based method can effectively remove the veiling light and outperforms other existing methods, even in dense turbid water. [ABSTRACT FROM AUTHOR]

A demand for division of focal plane (DoFP) polarization image sensors grows rapidly as nanofabrication technologies become mature. The DoFP sensor can output real time data of polarization information. In this paper, a novel visualization method for angle of polarization (AoP) is proposed for DoFP polarization image sensors. The data characteristics of AoP are analyzed, and strategies for a visualization method are proposed which conforms to the characteristics of AoP data. According to these strategies, we propose a visualization method for AoP data based on three dimensional HSI color space. This method uses intensity and saturation to characterize the magnitude of the angle between the polarization direction and the horizontal direction wherein the hue indicates the deflection direction. It is shown by the numerical simulation that the noise in the AoP image can be suppressed by our visualization method. In addition, the real-world experiment results are consistent with the numerical simulation and verify that the AoP image obtained by our method can suppress the influence of characterization noise, and the image is simple and intuitive, which is advantageous to human vision. The proposed method can be directly used for the commercialized DoFP polarization image sensor to display real-time AoP data. [ABSTRACT FROM AUTHOR]