Search

Optical metasurfaces, consisting of subwavelength‐scale meta‐atom arrays, hold great promise of overcoming the fundamental limitations of conventional optics. Due to their structural complexity, metasurfaces usually require high‐resolution yet slow and expensive fabrication processes. Here, using a metasurface polarimetric imaging device as an example, the photonic structures and the Nanoimprint lithography (NIL) processes are designed, creating two separate NIL molds over a patterning area of > 20 mm2 with designed Moiré alignment markers by electron‐beam writing, and further subsequently integrate silicon and aluminum metasurface structures on a chip. Uniquely, the silicon and aluminum metasurfaces are fabricated by using the nanolithography and 3D pattern‐transfer capabilities of NIL, respectively, achieving nanometer‐scale linewidth uniformity, sub‐200 nm translational overlay accuracy, and <0.017 rotational alignment error while significantly reducing fabrication complexity and surface roughness. The micro‐sized multilayer metasurfaces have high circular polarization extinction ratios as large as ≈20 and ≈80 in blue and red wavelengths. Further, the metasurface chip‐integrated CMOS imager demonstrates high accuracy in broad‐band, full Stokes parameter analysis in the visible wavelength ranges and single‐shot polarimetric imaging. This novel, NIL‐based, multilayered nanomanufacturing approach is applicable to the scalable production of large‐area functional structures for ultra‐compact optic, electronic, and quantum devices. [ABSTRACT FROM AUTHOR]

Polarimetric imaging technology plays a crucial role in de-scattering, particularly in the field of underwater image restoration. Circularly polarized light (or the underlying circular polarization memory effect) has been proven to better preserve the polarization characteristics of detected light. Utilizing circularly polarized light as illumination can further enhance the effectiveness of polarization de-scattering techniques. After rethinking the advantages of circularly polarized light, this paper proposes a new method for underwater polarimetric imaging restoration that leverages the pre-processing of polarized sub-images and the correlation of polarization characteristics (i.e., the angle of polarization and degree of polarization). Additionally, to address the challenge of selecting scattering light parameters due to uneven light fields in target scenes, an intensity adjustment factor search algorithm is designed. This algorithm eliminates the need for the manual selection of scattering light parameters, effectively solving the problem of uneven illumination in restoration results. A series of experiments demonstrate that, compared to traditional algorithms, the proposed method offers superior detail restoration and higher robustness. [ABSTRACT FROM AUTHOR]

The presence of numerous microscopic particles within haze contributes to the scattering of atmospheric light, leading to a notable degradation in the quality of captured images. Polarization, an intrinsic attribute of the light field, encapsulates a wealth of information concerning both targets and their surrounding environment and has demonstrated robust performance in dehazing real-world hazy images. In this study, we introduce an end-to-end dehazing network tailored for processing polarization-based hazy images. Leveraging the capabilities of 3D convolution, our novel POL-3D Encoder adeptly harnesses the correlation information situated within the polarization angle dimension, derived from fused polarization images. Furthermore, we integrate a Spatial Redundancy-Reducing module, effectively mitigating redundancy within feature maps across the spatial dimension. To assess the efficacy of our network and ensure its robustness, we curate a dataset rooted in the principles of polarization-based image formation. Experimental results conclusively demonstrate that our method achieves state-of-the-art performance on both synthetic data and real-world hazy images, thereby highlighting its potential for advancing image dehazing techniques. [ABSTRACT FROM AUTHOR]

The accurate identification of target imagery in the presence of sea fog is essential for the precise detection and comprehension of targets situated at sea. To overcome the issues encountered when applying traditional polarimetric dehazing methods to sea fog imagery, this paper proposes an improved polarimetric dehazing method. Initially, the methodology employs quartile-based selection on polarization difference images to ascertain atmospheric light at an infinite distance. Subsequently, the study describes a segmentation approach for sea–sky background images based on the degree of polarization. The results show that the image information entropy of the segmentation process improves by more than 6% compared to that of alternative methodologies, and the local contrast of the image is increased by more than 30% compared to that of the original foggy image. These outcomes confirm the effectiveness of the proposed dehazing methodology in addressing the challenges associated with sea fog imagery. [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. [ABSTRACT FROM AUTHOR]

This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications. The widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and interest. Polarization information is increasingly incorporated into convolutional neural networks (CNN) as a supplemental feature of objects to improve performance in computer vision task applications. Polarimetric imaging and deep learning can extract abundant information to address various challenges. Therefore, this article briefly reviews recent developments in data-driven polarimetric imaging, including polarimetric descattering, 3D imaging, reflection removal, target detection, and biomedical imaging. Furthermore, we synthetically analyze the input, datasets, and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and disadvantages. We also highlight the significance of data-driven polarimetric imaging in future research and development.

High‐speed and sensitive UV–Vis photodetectors have been constructed based on a typical 1D van der Waals material, antimony sulfide (Sb2S3). Impressively, the Sb2S3 nanowire photodetector demonstrates pronounced photosensitivity exhibiting a remarkable on/off ratio of ≈2800 under a power density of 318 mW cm−2. In addition, a high responsivity, an outstanding detectivity, and a short response/recovery time of 270 A W−1, 4.37 × 1013 Jones, and 10/12 ms are achieved. The competitive photosensitivity is associated with the intrinsic Mie resonance of the Sb2S3 nanowire, which is conducive to enhancing the coupling of the Sb2S3 photosensitive channel with incident light. By virtue of the unique 1D structural nature in both intrinsic and extrinsic perspectives, the Sb2S3 nanowire photodetectors manifest distinct polarization‐discriminating photoresponse with the optimal dichroic ratio reaching ≈7.2. Moreover, the Sb2S3 nanowire photodetectors demonstrate stable photoresponse from room temperature to 160 °C, and these nanodevices are durable against long‐term high‐temperature heating treatment at up to 300 °C. Taking advantage of the excellent thermal robustness, high‐temperature‐proof optoelectronic imaging and dual‐channel optical communication applications are demonstrated based on low‐dimensional van der Waals materials. On the whole, this study provides a new option for an advanced multifunctional optoelectronic system in extreme working environments. [ABSTRACT FROM AUTHOR]

The bionic polarimetric imaging navigation sensor (BPINS) is a navigation sensor that provides absolute heading, and it is of practical engineering significance to model the measurement error of BPINS. The existing BPINSs are still modeled using photodiode-based measurements rather than imaging measurements and are not modeled systematically enough. This paper proposes a measurement performance analysis method of BPINS that takes into account the geometric and polarization errors of the optical system. Firstly, the key error factors affecting the overall measurement performance of BPINS are investigated, and the Stokes vector-based measurement error model of BPINS is introduced. Secondly, based on its measurement error model, the effect of the error source on the measurement performance of BPINS is quantitatively analyzed using Rayleigh scattering to generate scattered sunlight as a known incident light source. The numerical results show that in angle of E-vector (AoE) measurement, the coordinate deviation of the principal point has a greater impact, followed by grayscale response inconsistency of CMOS and integration angle error of micro-polarization array, and finally lens attenuation; in degree of linear polarization (DoLP) measurement, the grayscale response inconsistency of CMOS has a more significant impact. This finding can accurately guide the subsequent calibration of BPINS, and the quantitative results provide an important theoretical reference for its optimal design. [ABSTRACT FROM AUTHOR]

In shallow tissues of the human body, pathological changes often occur, and there are several kinds of scattering media, such as mucosa, fat, and blood, present on the surface of these tissues. In such scattering environments, it is difficult to distinguish the location of the lesions using traditional attenuation-based imaging methods, while polarization-based imaging methods are more sensitive to this information. Therefore, in this paper, we conducted experiments using diluted milk to simulate biological tissues with scattering effects, illuminated with non-polarized light sources, and used an optimized robust polarization de-scattering algorithm for image processing. The results were qualitatively and quantitatively analyzed through local intensity comparison and visual fidelity functions, verifying the effectiveness of this algorithm under specific conditions. [ABSTRACT FROM AUTHOR]

Polarimetric imaging technology plays a crucial role in de-scattering, particularly in the field of underwater image restoration. Circularly polarized light (or the underlying circular polarization memory effect) has been proven to better preserve the polarization characteristics of detected light. Utilizing circularly polarized light as illumination can further enhance the effectiveness of polarization de-scattering techniques. After rethinking the advantages of circularly polarized light, this paper proposes a new method for underwater polarimetric imaging restoration that leverages the pre-processing of polarized sub-images and the correlation of polarization characteristics (i.e., the angle of polarization and degree of polarization). Additionally, to address the challenge of selecting scattering light parameters due to uneven light fields in target scenes, an intensity adjustment factor search algorithm is designed. This algorithm eliminates the need for the manual selection of scattering light parameters, effectively solving the problem of uneven illumination in restoration results. A series of experiments demonstrate that, compared to traditional algorithms, the proposed method offers superior detail restoration and higher robustness.

Heliostat mirror soiling is one of the significant factors for reduced optical efficiency of Concentrated Solar Power (CSP) fields. However, it remains challenging to determine the mirror soiling patterns across the large area of the CSP field. Here we propose a fast and field deployable inspection method to measure the heliostat mirror soiling levels based on polarization images in regular daylight settings without additional light sources. Under sunny and clear sky conditions, we have demonstrated accurate measurement of reflection efficiency (error ~1%) for mirrors with different soiling levels.

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]

The technological developments initiated in the early 21$^{\rm st}$ century have led to the implementation of “planet finders” instruments on 8-m class telescopes which are in operation since 2014-2015. Such facilities are at the inception of significant progresses in the study of exoplanetary systems by enabling high contrast imaging of the environment of young nearby stars. One of the most productive science in this field is certainly the observations at high angular resolution of circumstellar disks, from the very young gas-rich protoplanetary disks all the way to more elusive dust-rich debris disks. In this paper, we summarize the main results obtained for these two categories of objects focusing on their morphological characteristics, as well as on the measurement of optical properties of the small dust particles to which the near IR spectral range is sensitive to. We mostly provide exemples based on observations performed with the SPHERE instrument at the VLT.

The accurate identification of target imagery in the presence of sea fog is essential for the precise detection and comprehension of targets situated at sea. To overcome the issues encountered when applying traditional polarimetric dehazing methods to sea fog imagery, this paper proposes an improved polarimetric dehazing method. Initially, the methodology employs quartile-based selection on polarization difference images to ascertain atmospheric light at an infinite distance. Subsequently, the study describes a segmentation approach for sea–sky background images based on the degree of polarization. The results show that the image information entropy of the segmentation process improves by more than 6% compared to that of alternative methodologies, and the local contrast of the image is increased by more than 30% compared to that of the original foggy image. These outcomes confirm the effectiveness of the proposed dehazing methodology in addressing the challenges associated with sea fog imagery.

Polarization encoded images improve on conventional intensity imaging techniques by providing access to additional parameters describing the vector nature of light. In a polarimetric image, each pixel is related to a 4 × 1 vector named Stokes vector ( 3 × 1 in a linear configuration, which is the framework retained afterwards). Such images comprise a valuable set of physical information on the objects they contain, amplifying subsequently the accuracy of the analysis that can be done. A Stokes imaging polarimeter yields data named radiance images from which Stokes vectors are reconstructed, supposed to comply with a physical admissibility constraint. Classical estimation techniques such as pseudo-inverse approach exhibit defects, hampering any relevant physical interpretation of the scene: (i) first, due to their sensitivity to noise and errors that may contaminate the observed radiance images and that may then propagate to the evaluation of the Stokes vector components, thus justifying an ad hoc a posteriori treatment of Stokes vectors; (ii) second, in not taking this physical admissibility criterion explicitly into account. Motivated by this observation, the proposed contribution aims to provide a method of reconstruction addressing both issues, thus ensuring smoothness and spatial consistency of the reconstructed components, as well as compliance with the prescribed physical admissibility constraint. A by-product of the algorithm is that the resulting angle of polarization reflects more faithfully the physical properties of the materials present in the image. The mathematical formulation yields a non-smooth convex optimization problem that is then converted into a min–max problem and solved by the generic Chambolle–Pock primal-dual algorithm. Several mathematical results (such as existence/uniqueness of the minimizer of the primal problem, existence of a saddle point to the associated Lagrangian, etc.) are supplied and highlight the well-posed character of the modelling. Experiments demonstrate that our method provides significant improvements (i) over the least square-based method both in terms of quantitative criteria (physical admissibility constraint automatically met) and qualitative assessment (spatial regularization/coherency), (ii) over the physical consistency of related relevant polarimetric parameters such as the angle and degree of polarization, (iii) robustness of the method when applied on real outdoor scenes acquired in degraded conditions (poor weather conditions, etc.). [ABSTRACT FROM AUTHOR]

In computational photography, high dynamic range (HDR) imaging refers to the family of techniques used to recover a wider range of intensity values compared to the limited range provided by standard sensors. Classical techniques consist of acquiring a scene-varying exposure to compensate for saturated and underexposed regions, followed by a non-linear compression of intensity values called tone mapping. Recently, there has been a growing interest in estimating HDR images from a single exposure. Some methods exploit data-driven models trained to estimate values outside the camera's visible intensity levels. Others make use of polarimetric cameras to reconstruct HDR information without exposure bracketing. In this paper, we present a novel HDR reconstruction method that employs a single PFA (polarimetric filter array) camera with an additional external polarizer to increase the scene's dynamic range across the acquired channels and to mimic different exposures. Our contribution consists of a pipeline that effectively combines standard HDR algorithms based on bracketing and data-driven solutions designed to work with polarimetric images. In this regard, we present a novel CNN (convolutional neural network) model that exploits the underlying mosaiced pattern of the PFA in combination with the external polarizer to estimate the original scene properties, and a second model designed to further improve the final tone mapping step. The combination of such techniques enables us to take advantage of the light attenuation given by the filters while producing an accurate reconstruction. We present an extensive experimental section in which we validate the proposed method on both synthetic and real-world datasets specifically acquired for the task. Quantitative and qualitative results show the effectiveness of the approach when compared to state-of-the-art methods. In particular, our technique exhibits a PSNR (peak signal-to-noise ratio) on the whole test set equal to 23 dB, which is 18 % better with respect to the second-best alternative. [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.

The bionic polarimetric imaging navigation sensor (BPINS) is a navigation sensor that provides absolute heading, and it is of practical engineering significance to model the measurement error of BPINS. The existing BPINSs are still modeled using photodiode-based measurements rather than imaging measurements and are not modeled systematically enough. This paper proposes a measurement performance analysis method of BPINS that takes into account the geometric and polarization errors of the optical system. Firstly, the key error factors affecting the overall measurement performance of BPINS are investigated, and the Stokes vector-based measurement error model of BPINS is introduced. Secondly, based on its measurement error model, the effect of the error source on the measurement performance of BPINS is quantitatively analyzed using Rayleigh scattering to generate scattered sunlight as a known incident light source. The numerical results show that in angle of E-vector (AoE) measurement, the coordinate deviation of the principal point has a greater impact, followed by grayscale response inconsistency of CMOS and integration angle error of micro-polarization array, and finally lens attenuation; in degree of linear polarization (DoLP) measurement, the grayscale response inconsistency of CMOS has a more significant impact. This finding can accurately guide the subsequent calibration of BPINS, and the quantitative results provide an important theoretical reference for its optimal design.

In shallow tissues of the human body, pathological changes often occur, and there are several kinds of scattering media, such as mucosa, fat, and blood, present on the surface of these tissues. In such scattering environments, it is difficult to distinguish the location of the lesions using traditional attenuation-based imaging methods, while polarization-based imaging methods are more sensitive to this information. Therefore, in this paper, we conducted experiments using diluted milk to simulate biological tissues with scattering effects, illuminated with non-polarized light sources, and used an optimized robust polarization de-scattering algorithm for image processing. The results were qualitatively and quantitatively analyzed through local intensity comparison and visual fidelity functions, verifying the effectiveness of this algorithm under specific conditions.

The absorption and scattering of impurity particles in turbid water cause the target signal light to be attenuated and to produce backscattered light, resulting in the reduced quality of underwater polarimetric imaging. As water turbidity increases, the effect of backscattered light becomes greater, making polarization imaging in highly turbid water a challenge. Theory and experiment show that the increase in the intensity of backscattered light leads to high noise gain in the underwater active polarization imaging model. In order to enhance image contrast and suppress noise gain in highly turbid water, we propose an underwater imaging enhancement method that appropriately combines the non-physical and physical models. The method uses contrast limited adaptive histogram equalization (CLAHE) for a certain number of cross-linear images (Imin) before calculating their polarization enhancement images, and it constructs joint filtering (multi-frame averaging and bilateral filtering) to suppress the high noise gain introduced by the imaging model and CLAHE. The experimental results in highly turbid water validate the rationality and feasibility of the proposed method, and the comparative processing results (52.7~98.6 NTU) outperform those of the conventional non-physical and physical model methods. The method maintains the complexity of the system and facilitates the application of conventional polarimetric imaging in harsher underwater environments. [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.

The traditional polarization imaging techniques cannot obtain the polarization characteristics with global variation, and the reconstruction of the target with different optical properties concurrently is seriously distorted. In this work, we overcome the aforementioned shortcomings by using the polar decomposition method to estimate the Mueller matrix of the target in the full field of view. Simultaneously, the illumination light with globally varying polarization state is calculated to suppress the noise caused by computational imaging. The descattering imaging experiments through turbid water indicate that the images recovered by our method present the similarity to the targets without scattering, and the noise is suppressed to some extent. Further, we prove the superiority of the proposed method by comparing with other polarization underwater imaging strategies.

This paper addresses the objectives in a complex context: the polarization mathematical and physical model of the basic components of the target/background is constructed by simulation modeling. The image fusion experiment is carried out based on a two-dimensional wavelet transformation. A high-resolution polarization imaging instrument was developed, and static experiments were carried out with polarization, infrared, and visible cameras. The result shows that using polarization imaging detection technology to detect physical evidence targets in complex backgrounds can effectively improve target contrast. It can enrich the target information, improve image quality, and improve target detection accuracy. [ABSTRACT FROM AUTHOR]

Estimating the degree of polarization (DOP) of target signal light ( p targ ) is a critical step in the underwater polarization differential imaging process. It is challenging to accurately estimate the p targ of objects containing multiple attributes in the imaging range. Inspired by the optical polarization characteristics of objects with different DOP, we find a prior of connected domains (CDs) of objects in polarization orthogonal images. Based on this prior, the CDs of low-DOP (LDOP) and high-DOP (HDOP) objects are extracted from the darkest image ( I min ) and brightest image ( I max ) without additional image acquisition. The p targ of LDOP and HDOP objects in the scene can be estimated through the extracted CDs, which realizes the transition from global optimum estimation in the traditional method to local optimum estimation. According to the estimated p targ , the enhanced results of the entire image marked by p targ can be obtained after fusing the enhanced results of the objects in each CD. Experimental results of simple and complex combination scenes of objects show that our method can recognize CDs of LDOP and HDOP objects. We obtained the enhanced results of the entire image, verified the feasibility and robustness of our method, and found that the CDs of LDOP and HDOP objects could be effectively extracted in up to 34.3 NTU of turbid water. The local optimum improves the application ability of underwater polarization imaging in unpredictable scenarios, while the enhanced result of target labeling has potential application value in underwater target recognition and classification. [ABSTRACT FROM AUTHOR]

The traditional polarization imaging techniques cannot obtain the polarization characteristics with global variation, and the reconstruction of the target with different optical properties concurrently is seriously distorted. In this work, we overcome the aforementioned shortcomings by using the polar decomposition method to estimate the Mueller matrix of the target in the full field of view. Simultaneously, the illumination light with globally varying polarization state is calculated to suppress the noise caused by computational imaging. The descattering imaging experiments through turbid water indicate that the images recovered by our method present the similarity to the targets without scattering, and the noise is suppressed to some extent. Further, we prove the superiority of the proposed method by comparing with other polarization underwater imaging strategies. [ABSTRACT FROM AUTHOR]

Individual anisotropic two-dimensional (2D) materials have been widely applied for developing polarization-sensitive photodetectors, but they often suffer from limitations in photoresponsivity, detection range, etc. To overcome these challenges, van der Waals (vdW) heterostructures created by stacking different 2D materials provide a promising solution to enhance the performance of the photoelectronic device. In this work, a novel polarization-sensitive photodetector is developed by leveraging a heterojunction formed by InBiSe 3 and anisotropic ReS 2 nanoflakes. The InBiSe 3 /ReS 2 vdW heterostructure devices exhibit excellent photodetection performance with a high photoresponsivity (R) of 7.68 A W -1 and a specific detectivity (D * ) up to 1.26 × 10 11 Jones as well as an external quantum efficiency (EQE) of 1790% under 532 nm laser irradiation. Additionally, benefiting from the broadband light absorption of InBiSe 3 crystals together with the pronounced anisotropic electronic and optical characteristics of ReS 2 flakes, the devices demonstrate a broad spectral response range from 402 to 1006 nm with a distinct polarization sensitivity of 1.24. Moreover, the devices exhibit extraordinary optical communication and high contrast polarimetric imaging capacity. This work demonstrates the enhanced photodetection performance with the InBiSe 3 /ReS 2 vdW heterostructures operating in a photoconductive mode and illustrates promising application of these heterostructures in integrated optoelectronic systems., (© 2024 Wiley‐VCH GmbH.)

Miniaturized polarimetric photodetectors based on anisotropic two-dimensional materials attract potential applications in ultra-compact polarimeters. However, these photodetectors are hindered by the small polarization ratio values and complicated artificial structures. Here, a novel polarization photodetector based on in-sublattice carrier transition in the CdSb 2 Se 3 Br 2 /WSe 2 heterostructure, with a giant and reconfigurable PR value, is demonstrated. The unique periodic sublattice structure of CdSb 2 Se 3 Br 2 features an in-sublattice carrier transition preferred along Sb 2 Se 3 chains. Leveraging on the in-sublattice carrier transition in the CdSb 2 Se 3 Br 2 /WSe 2 heterostructure, gate voltage has an anisotropic modulation effect on the band alignment of heterostructure along sublattice. Consequently, the heterostructure exhibits a polarization-tunable photo-induced threshold voltage shift, which provides reconfigurable PR values from positive (unipolar regime) to negative (bipolar regime), covering all possible numbers (1→+∞/-∞→-1). Using this anisotropic photovoltaic effect, gate-tunable polarimetric imaging is successfully implemented. This work provides a new platform for developing next-generation highly polarimetric optoelectronics., (© 2024 Wiley‐VCH GmbH.)

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.

In computational photography, high dynamic range (HDR) imaging refers to the family of techniques used to recover a wider range of intensity values compared to the limited range provided by standard sensors. Classical techniques consist of acquiring a scene-varying exposure to compensate for saturated and underexposed regions, followed by a non-linear compression of intensity values called tone mapping. Recently, there has been a growing interest in estimating HDR images from a single exposure. Some methods exploit data-driven models trained to estimate values outside the camera’s visible intensity levels. Others make use of polarimetric cameras to reconstruct HDR information without exposure bracketing. In this paper, we present a novel HDR reconstruction method that employs a single PFA (polarimetric filter array) camera with an additional external polarizer to increase the scene’s dynamic range across the acquired channels and to mimic different exposures. Our contribution consists of a pipeline that effectively combines standard HDR algorithms based on bracketing and data-driven solutions designed to work with polarimetric images. In this regard, we present a novel CNN (convolutional neural network) model that exploits the underlying mosaiced pattern of the PFA in combination with the external polarizer to estimate the original scene properties, and a second model designed to further improve the final tone mapping step. The combination of such techniques enables us to take advantage of the light attenuation given by the filters while producing an accurate reconstruction. We present an extensive experimental section in which we validate the proposed method on both synthetic and real-world datasets specifically acquired for the task. Quantitative and qualitative results show the effectiveness of the approach when compared to state-of-the-art methods. In particular, our technique exhibits a PSNR (peak signal-to-noise ratio) on the whole test set equal to 23 dB, which is 18% better with respect to the second-best alternative.

Road scene analysis is a fundamental task for both autonomous vehicles and ADAS systems. Nowadays, one can find autonomous vehicles that are able to properly detect objects in the scene in good weather conditions; however, some improvements still need to be done when the visibility is altered. People claim that using some non-conventional sensors such as, infra-red or Lidar, combined with classical vision, enhances road scene analysis in optimal weather conditions. In this work, we present the improvements achieved using polarimetric imaging in the complex situation of some adverse weather conditions. This rich modality is known for its ability to describe an object not only by its intensity information, even under poor illumination or strong reflection. The experimental results have shown that, using a new multimodal dataset, polarimetric imaging was able to provide generic features for both good weather conditions and adverse weather conditions, especially fog. By combining polarimetric images with an adapted learning model, the different detection tasks under fog were improved by about 15% to 44%. [ABSTRACT FROM AUTHOR]

Polarisation Filter Array (PFA) cameras allow the analysis of light polarisation state in a simple and cost-effective manner. Such filter arrays work as the Bayer pattern for colour cameras, sharing similar advantages and drawbacks. Among the others, the raw image must be demosaiced considering the local variations of the PFA and the characteristics of the imaged scene. Non-linear effects, like the cross-talk among neighbouring pixels, are difficult to explicitly model and suggest the potential advantage of a data-driven learning approach. However, the PFA cannot be removed from the sensor, making it difficult to acquire the ground-truth polarization state for training. In this work we propose a novel CNN-based model which directly demosaics the raw camera image to a per-pixel Stokes vector. Our contribution is twofold. First, we propose a network architecture composed by a sequence of Mosaiced Convolutions operating coherently with the local arrangement of the different filters. Second, we introduce a new method, employing a consumer LCD screen, to effectively acquire real-world data for training. The process is designed to be invariant by monitor gamma and external lighting conditions. We extensively compared our method against algorithmic and learning-based demosaicing techniques, obtaining a consistently lower error especially in terms of polarisation angle. [ABSTRACT FROM AUTHOR]

This work presents a study on the optical applications of chemical vapor deposition‐grown Sb2Se3 nanowires in polarized single nanowire photodetectors. High‐quality Sb2Se3 nanowires are obtained with diameters as small as ≈15 nm, which is the first report for ultrathin Sb2Se3 nanowires. The fabricated Sb2Se3 nanowire‐based photodetector presents a low shot noise of ≈ 9 × 10–16 A Hz–1/2, a large signal/noise ratio of 1436.55, a high responsivity of 3.61 A W–1, and a high specific detectivity of 2.36 × 1011 Jones, which can be attributed to the high‐quality crystalline nanowires obtained. More interestingly, the Sb2Se3 nanowire‐based photodetectors exhibit broadband polarized photoresponse to incident light with wavelengths ranging from visible to near‐infrared (532 – 830 nm). A linearly dichroic ratio of 1.71 is obtained for the 830 nm light illumination. The Sb2Se3 nanowire detectors also present appropriate polarimetric imaging quality, revealing the potential of Sb2Se3 nanowires for polarimetric imaging applications. [ABSTRACT FROM AUTHOR]

Due to its insensitivity to light intensity and the capability to capture multidimensional information, polarimetric imaging technology has been proven to have advantages over traditional intensity-based imaging techniques for object detection tasks in adverse environmental conditions, particularly in road traffic scenarios. Recently, with the rapid development of artificial intelligence technology, deep learning (DL)-powered object detection techniques can further enhance recognition accuracy and algorithm robustness. This improvement is made possible by the ability of DL technology to leverage large datasets and extract deeper levels of target-specific features. However, constructing large-scale polarimetric datasets poses challenges as obtaining polarimetric information requires multiple captures of intensity images. In other words, the workload is several times higher compared to traditional imaging techniques. To address the current scarcity of polarimetric datasets and evaluate the practical performance of various algorithms on polarimetric datasets, this paper proposes a Polarimetric Object Detection Benchmark (PODB) dataset. The PODB provides an integrated quality evaluation framework for DL-based object detection algorithms in complex road scenes by incorporating polarimetric imaging. Besides, we conducted extensive object detection experiments using the PODB, which allowed for a comprehensive validation and performance evaluation of effective benchmark algorithms. Furthermore, a physics-based multi-scale image fusion cascaded object detection neural network model is proposed. By combining the multidimensional information provided by polarized images with an adaptive learning multi-decision object detection neural network model, the recognition accuracy of complex road scenes in adverse weather conditions has been improved by approximately 10%. Additionally, we anticipate that PODB will serve as an effective platform for evaluating and comparing the performance of object detection algorithms, as well as providing researchers with a baseline for future studies in developing new DL-based methods. • Polarimetric imaging excels in challenging conditions, like road traffic. • Scarce polarimetric road data due to acquisition difficulties. • Proposed Polarimetric Object Detection Benchmark dataset for deep learning-based algorithms. • Introduction of a novel multi-scale image fusion cascaded neural network model. • Achieving a 10% accuracy boost for complex, adverse weather road scenes. [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.

This article presents the computation of the backscattered field in simulation of synthetic aperture radar (SAR) raw data. We develop an improved Kirchhoff approximation to estimate the surface fields induced by the incident waves. The improved Kirchhoff approximation is a second-order iterative solution of the integral equations governing the targets’ electric and magnetic current densities. In computing the second-order re-radiated fields, we applied the shoot-bouncing-ray (SBR) technique to enhance propagation path tracking efficiency. The geometrical theory of diffraction (GTD) was employed to account for the diffraction fields from the edges and wedges, which constitute the total scattered fields collected by SAR in synthetic aperture. As the SAR moves along the azimuth direction, the backscattered signal computation is repeated as the antenna beam crosses the targets. This procedure demands heavy computational resources but requires no priori assumptions of radar cross-sections (RCSs) nor speckle statistics. The raw data is generated as an output signal of the SAR system response into which the backscattered signal is input. We chose three types of the target to demonstrate our approach to confirm the effectiveness. The first set of targets are three dihedral reflectors – two of them were rotated to constitute three unique scattering matrices. The results show that the polarimetric information, both relative amplitudes and phases, are well–preserved. The second target is a rough surface with exponential power spectral density and Gaussian height probability density. We examined image speckle statistics of multi-looking image. The third target type is an electrically large cargo ship (container) sitting over a sea surface. Polarimetric analysis via the Pauli and Y4R decompositions reveals that the polarimetric features are well preserved. Simulation results demonstrate that the present approach is fully coherent in streamlining the data flow from backscattered field to complex single look images within the SAR imaging scene. [ABSTRACT FROM AUTHOR]

The estimation of the degree of polarization of backscattered light and target light is the key to realize scattering removal in underwater polarimetric imaging. In this paper, a new polarization scattering removal method is proposed by realizing polarimetric calculation of the target light with each pixel of the scene automatically, which is conducive to the restoration of the details of underwater scene. The global model of pixel level is established by utilizing the gradient prior of the polarization information of the total radiance image. Different from the previous study that the degree of polarization of the target light is a constant, the global calculation method can solve the global variable image of the degree of polarization of the target light and it is used for reconstruction experiments of underwater real-world scene to verify the effectiveness of the algorithm. The experimental results demonstrate that the global pixel calculation method has obvious effect on image detail recovery of the underwater scene to realize the clear underwater vision. The influence of the backscattered light on underwater imaging is well suppressed and image contrast has been significantly improved, which provides a new method for enhancing performance of underwater polarimetric imaging with better vision.

Polarimetric imaging is an effective way for enhancing the image quality in underwater environments. Previous polarization-based methods for underwater imaging actually involve both optical and digital processing, while compared with digital processing, the importance of the optical method is not clear. In this paper, we propose a polarimetric method for underwater imaging based on the Mueller imager. In this method, in order to optimally filter out the backscattered light and enhance the image quality, the polarization states of illumination light together with the polarization filter in front of the camera are synergistically modulated in an optical way. In other words, the quality of images captured by the camera can be directly improved without any digital processing. A series of experimental results show the effectiveness of the proposed method, which indeed verifies its superiority in scattering media.

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.

Light emitted from a source into a scene can undergo complex interactions with multiple scene surfaces of different material types before being reflected towards a detector. During this transport, every surface reflection and propagation is encoded in the properties of the photons that ultimately reach the detector, including travel time, direction, intensity, wavelength and polarization. Conventional imaging systems capture intensity by integrating over all other dimensions of the incident light into a single quantity, hiding this rich scene information in these aggregate measurements. Existing methods are capable of untangling these measurements into their spatial and temporal dimensions, fueling geometric scene understanding tasks. However, examining polarimetric material properties jointly with geometric properties is an open challenge that could enable unprecedented capabilities beyond geometric scene understanding, allowing for material-dependent scene understanding and imaging through complex transport, such as macroscopic scattering. In this work, we close this gap, and propose a computational light transport imaging method that captures the spatially- and temporally-resolved complete polarimetric response of a scene, which encodes rich material properties. Our method hinges on a novel 7D tensor theory of light transport. We discover low-rank structure in the polarimetric tensor dimension and propose a data-driven rotating ellipsometry method that learns to exploit redundancy of polarimetric structure. We instantiate our theory with two imaging prototypes: spatio-polarimetric imaging and coaxial temporal-polarimetric imaging. This allows us, for the first time, to decompose scene light transport into temporal, spatial, and complete polarimetric dimensions that unveil scene properties hidden to conventional methods. We validate the applicability of our method on diverse tasks, including shape reconstruction with subsurface scattering, seeing through scattering media, untangling multi-bounce light transport, breaking metamerism with polarization, and spatio-polarimetric decomposition of crystals. [ABSTRACT FROM AUTHOR]

Polarimetric imaging has been studied and applied to the problem of visibility restoration in various scenarios such as haze, mist and underwater. Although studies have shown that under certain conditions, circular polarimetric imaging has certain advantages over linear polarimetric imaging, however, for a complex environment containing both scattering medium and object, the performance of linear and circular polarimetric imaging is affected by many factors. In this paper, the propagation of linear and circular polarized light in the scattering medium is theoretically analyzed, then the simulation experiments under different experimental conditions are carried out and the conclusions are summarized. In order to validate the simulation results, the measurement experiments are carried out in dynamic smoke scenarios with different smoke concentrations. The results show that, the propagation of the polarized light, especially the circular polarized light, is determined by medium conditions. Generally, both the linear and circular polarimetric imaging had an ability to reduce the image degradation caused by smoke, however, under some certain environment conditions, unlike the linear polarized channels, the difference between the orthogonal circular polarized channels may be approached or even reversed, which may limit the circular polarization-based difference imaging and visibility restoration performance.

The absorption and scattering of impurity particles in turbid water cause the target signal light to be attenuated and to produce backscattered light, resulting in the reduced quality of underwater polarimetric imaging. As water turbidity increases, the effect of backscattered light becomes greater, making polarization imaging in highly turbid water a challenge. Theory and experiment show that the increase in the intensity of backscattered light leads to high noise gain in the underwater active polarization imaging model. In order to enhance image contrast and suppress noise gain in highly turbid water, we propose an underwater imaging enhancement method that appropriately combines the non-physical and physical models. The method uses contrast limited adaptive histogram equalization (CLAHE) for a certain number of cross-linear images (Imin) before calculating their polarization enhancement images, and it constructs joint filtering (multi-frame averaging and bilateral filtering) to suppress the high noise gain introduced by the imaging model and CLAHE. The experimental results in highly turbid water validate the rationality and feasibility of the proposed method, and the comparative processing results (52.7~98.6 NTU) outperform those of the conventional non-physical and physical model methods. The method maintains the complexity of the system and facilitates the application of conventional polarimetric imaging in harsher underwater environments.

Passive millimeter-wave and terahertz (MMW/THz) imaging is a significant technique used in personal security inspection and scene surveillance. However, a human body generally generates an obvious reflection on the floor, which may have negative influences on target detection and observer vision. In this article, we present a physically based method using dual-polarization feature and saliency information to solve the problem of reflection removal. The physical principle of a special dual-polarization phenomenon has been revealed by theoretical calculation and simulation analysis. By combining the second Stokes parameter (TQ) and modified linear polarization ratio (mLPR), a new feature parameter has been created to extract reflections of human bodies. By using a saliency analysis, the false positive alarms are eliminated completely. The experimental results demonstrate the robustness and effectiveness of the proposed method using various measured MMW/THz images. [ABSTRACT FROM AUTHOR]

To improve the robustness of current polarimetric dehazing scheme in the condition of low degree of polarization, we report a polarimetric dehazing method based on the image fusion technique and adaptive adjustment algorithm which can operate well in many different conditions. A splitting focus plane linear polarization camera was employed to grab the images of four different polarization directions, and the haze was separated from the hazy images by low-pass filtering roughly. Then the image fusion technique was used to optimize the method of estimating the transmittance map. To improve the quality of the dehazed images, an adaptive adjustment algorithm was introduced to adjust the illumination distribution of the dehazed images. The outdoor experiments have been implemented and the results indicated that the presented method could restore the target information obviously, and both the visual effect and quantitative evaluation have been enhanced. [ABSTRACT FROM AUTHOR]

This paper addresses the objectives in a complex context: the polarization mathematical and physical model of the basic components of the target/background is constructed by simulation modeling. The image fusion experiment is carried out based on a two-dimensional wavelet transformation. A high-resolution polarization imaging instrument was developed, and static experiments were carried out with polarization, infrared, and visible cameras. The result shows that using polarization imaging detection technology to detect physical evidence targets in complex backgrounds can effectively improve target contrast. It can enrich the target information, improve image quality, and improve target detection accuracy.

The underwater image restoration based on polarization information has achieved good results in improving the image quality in scattering media. However, the previous methods are difficult to obtain the true distribution of degree of polarization in the scene. In this paper, we combine synthetic aperture imaging with polarimetric imaging, and propose a method for retrieving radiation of object based on the degree of polarization and intensity of backscattering at the multi-view image. In addition, compared with the previous methods, the proposed method can achieve simultaneous acquisition of four-dimensional light field information and polarization information, effectively increasing the information dimension obtained by single imaging. In order to verify the effectiveness and superiority of the proposed method, we have established a relevant experimental platform and compared with the experimental results of the previous methods, and obtained the expected experimental results.

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.