Polarimetric Images Research Articles

Sparse representation-based demosaicking method for joint chromatic and polarimetric imagery

Division of focal plane (DoFP) polarization camera has become mainstream among various polarimetric imaging systems whose sensor is equipped with Bayer filter and directional polarization filter, so joint chromatic and polarimetric image demosaicking is essential. However, since 15 or 14 pixels are missed out of 16 pixels in color polarization mosaic images, it’s challenging to adopt a method to reconstruct full-resolution images well. In this paper, we propose a demosaicking model based on sparse coding. The model introduces RGB-polarization channels correlation, adaptive sub-dictionaries and non-local self-similarity restrictions, and combines them into an optimal problem to solve. The experimental results, including reconstructing full-resolution synthetic images and camera captured real scenes, demonstrate that our proposed method outperforms the current state-of-the-art method in terms of quantitative measures and visual quality.

Using Machine Learning to link black hole accretion flows with spatially resolved polarimetric observables

ABSTRACT We introduce a new library of 535 194 model images of the supermassive black holes and Event Horizon Telescope (EHT) targets Sgr A* and M87*, computed by performing general relativistic radiative transfer calculations on general relativistic magnetohydrodynamics simulations. Then to infer underlying black hole and accretion flow parameters (spin, inclination, ion-to-electron temperature ratio, and magnetic field polarity), we train a random forest machine learning model on various hand-picked polarimetric observables computed from each image. Our random forest is capable of making meaningful predictions of spin, inclination, and the ion-to-electron temperature ratio, but has more difficulty inferring magnetic field polarity. To disentangle how physical parameters are encoded in different observables, we apply two different metrics to rank the importance of each observable at inferring each physical parameter. Details of the spatially resolved linear polarization morphology stand out as important discriminators between models. Bearing in mind the theoretical limitations and incompleteness of our image library, for the real M87* data, our machinery favours high-spin retrograde models with large ion-to-electron temperature ratios. Due to the time-variable nature of these targets, repeated polarimetric imaging will further improve model inference as the EHT and next-generation (EHT) continue to develop and monitor their targets.

Efficient machine learning method for spatio-temporal water surface waves reconstruction from polarimetric images

Accurate and cost-effective sea state measurements, in terms of spatio-temporal distribution of water surface elevation (water waves), is of great interest for scientific research and various engineering, industrial, and recreational applications. To this end, numerous measurement techniques have been developed over the years. None of these techniques, however, are universally applicable across various ocean and laboratory conditions and none provide near-real-time data. We utilized the latest advances in polarimetric imaging to develop a new remote sensing method based on machine learning methodology and polarimetric reflection measurements for inferring surface waves elevation and slope. The method utilizes a newly available, inexpensive polarimetric camera providing images of the water surface in a high spatio-temporal resolution at several linear polarization angles. Algorithms based on artificial neural networks (s) are then trained to obtain high-resolution reconstructions of the water surface slope state from those images. The s are trained on laboratory-collected supervised datasets of prescribed mechanically generated monochromatic wave trains and tested on a stochastic wave field of JONSWAP spectral shape. The proposed method, based on inferring the surface slope from polarimetric images, provides a dense estimate of the water surface. The results of this study pave the way for the development of accurate and cost-effective near-real-time remote sensing tools for both laboratory and open sea wave measurements.

Automatic Ship Detection Using PolSAR Imagery and the Double Scatterer Model

In ship detection by means of Polarimetric SAR imagery, a very promising feature is the characterization of the pixels of the ship based on the elementary scattering mechanisms that can be extracted using different decomposition algorithms. Elementary scattering mechanisms provide information regarding the physical, electrical and geometrical properties of the scatterers in each Polarimetric SAR pixel. In this work, the newly established algorithm of the Double Scatterer Model is applied to interpret each pixel of the Polarimetric SAR image with the contributions of two elementary scattering mechanisms, namely, primary and secondary. The main idea is to construct a binary image while preserving the rich information content in order to proceed in simple and fast image processing for target detection. The present algorithm is applied to datasets with different inherent characteristics acquired by Radarsat-2 and ALOS-PALSAR. The results presented by this new perspective on ship monitoring are remarkable.

Innovative Solutions Based on the EM-Algorithm for Covariance Structure Detection and Classification in Polarimetric SAR Images

This article addresses the challenge of identifying the polarimetric covariance matrix (PCM) structures associated with a polarimetric synthetic aperture radar (SAR) image. Interestingly, such information can be used, for instance, to improve the scene interpretation or to enhance the performance of (possibly PCM-based) segmentation algorithms as well as other kinds of methods. To this end, a general framework to solve a multiple hypothesis test is introduced with the aim to detect and classify contextual spatial variations in polarimetric SAR images. Specifically, under the null hypothesis, only one unknown structure is assumed for data belonging to a two-dimensional spatial sliding window, whereas under each alternative hypothesis, data are partitioned into subsets sharing different PCM structures. The problem of partition estimation is solved by resorting to hidden random variables representative of covariance structure classes and the expectation–maximization algorithm. The effectiveness of the proposed detection strategies is demonstrated on both simulated and real polarimetric SAR data also in comparison with existing classification algorithms.

Active Polarization Imaging for Cross-Linear Image Histogram Equalization and Noise Suppression in Highly Turbid Water

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.

Metasurface-Enabled 3-in-1 Microscopy.

Edge enhancement and polarization detection are critical to image transparent or low-contrast samples. However, currently available systems are limited to performing only a single functionality. To meet the requirement of system integration, there is a pressing need for a microscope with multiple functionalities. Here, we propose and develop a microscope with three different functionalities based on spatial multiplexing and polarization splitting. A novel geometric metasurface (MS) is used to realize a spiral phase profile and two phase gradient profiles along two vertical directions, which can perform such an extremely challenging optical task. This is the first demonstration of a 3-in-1 microscope that can simultaneously obtain five images with different optical properties in an imaging plane for the same sample. Imaging experiments with different samples verify its capability to simultaneously perform edge imaging, polarimetric imaging, and conventional microscope imaging. Benefiting from the compactness and multifunctionality of the optical MS device, the integration does not increase the volume of the microscope. This approach can enable users to visualize the multiple facets of samples in real-time.

Event-based imaging polarimeter simulation with a single DoFP image.

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

Optimization and commissioning of the EPIC commensal radio transient imager for the long wavelength array

ABSTRACT Next-generation aperture arrays are expected to consist of hundreds to thousands of antenna elements with substantial digital signal processing to handle large operating bandwidths of a few tens to hundreds of MHz. Conventionally, FX correlators are used as the primary signal processing unit of the interferometer. These correlators have computational costs that scale as $\mathcal {O}(N^2)$ for large arrays. An alternative imaging approach is implemented in the E-field Parallel Imaging Correlator (EPIC) that was recently deployed on the Long Wavelength Array station at the Sevilleta National Wildlife Refuge (LWA-SV) in New Mexico. EPIC uses a novel architecture that produces electric field or intensity images of the sky at the angular resolution of the array with full or partial polarization and the full spectral resolution of the channelizer. By eliminating the intermediate cross-correlation data products, the computational costs can be significantly lowered in comparison to a conventional FX or XF correlator from $\mathcal {O}(N^2)$ to $\mathcal {O}(N \log N)$ for dense (but otherwise arbitrary) array layouts. EPIC can also lower the output data rates by directly yielding polarimetric image products for science analysis. We have optimized EPIC and have now commissioned it at LWA-SV as a commensal all-sky imaging back-end that can potentially detect and localize sources of impulsive radio emission on millisecond timescales. In this article, we review the architecture of EPIC, describe code optimizations that improve performance, and present initial validations from commissioning observations. Comparisons between EPIC measurements and simultaneous beam-formed observations of bright sources show spectral-temporal structures in good agreement.

Polarization calibration assessment for a broadband imaging polarimeter based on a division of aperture architecture.

This article intends to provide all the experimental insights and analyze the best polarimetric calibration method for a division of aperture polarimetric imager considering the different implications it has on the experimental set-up and its performance. Polarimetric cameras require careful calibration for the correct measurement of polarization information. The calibration methods are introduced, intermediate results are presented, and the ability of the set-up to estimate Stokes vectors and Mueller matrices of the samples in passive and active imaging modes is evaluated. Polarization information recovery achieves accuracy errors below the 10% for all polarization modes when the Data Reduction Matrix or the Eigenvalue Calibration Method are used. Such performance, however, degrades significantly when using the Polarizer Calibration Method. To the best of our knowledge, this is the first time such a detailed comparison of calibration methods is presented in the literature, and it is also the first time the Polarizer Calibration Method is applied to a division of aperture polarimeter.

Residual network based on entropy-anisotropy-alpha target decomposition for polarimetric SAR image classification

Residual network based on entropy-anisotropy-alpha target decomposition for polarimetric SAR image classification

LE2Pol: the Leicester dual-beam imaging polarimeter

Abstract We describe the development, construction, data analysis, initial calibration, and first results of the Leicester dual-beam imaging polarimeter (LE2Pol), an inexpensive dual-beam optical imaging polarimeter. LE2Pol is designed for a 20-inch telescope at the observatory of the University of Leicester, but can also be used as a visiting instrument on a wide range of small telescopes (≲1 m). We show how simple imaging polarimeters on small telescopes can be used to provide useful scientific and educational data at low cost.

MAPPING OF URBAN FLOOD INUNDATION USING 3D DIGITAL SURFACE MODEL AND SENTINEL-1 IMAGES

Abstract. Flooding in urban areas poses serious risks to citizens, infrastructures, and transportation. Precise and real-time delineation of the inundated areas is crucial for a better understanding of the extent of damage and high-risk areas and people evacuation actions. It also increases citizens' awareness that living in areas with high flood risk. Yazd city is characterized by low rainfall (<70 mm/yr) and the desert climate is considered the study area of this research. This city encountered a flash flood event that was generated by severe rainfall with a depth of 75 mm in 3hr (i.e., the intensity of 25 mm/hr) on July 29, 2022. Many strategic infrastructures of this city especially the railway station were flooded, which caused heavy casualties and financial losses. This study aims to monitor the flood inundated areas of Yazd city due to this flood event using remote sensing. In this research, the Sentinel-1 polarimetric radar images and the 3D model of the Yazd city surf ace were used to delineate the flooded areas. The field information of the flooded areas and the available Sentinel-1 images during or near the occurrence time of maximum flood extension were adopted. The Convolutional Neural Network (CNN) model in combination with the 3D model of the studied area was used to identify the flooded pixels in the city of Yazd. The results showed that the adopted 3D model and CNN algorithm indicated a good ability to identify flooded areas with an accuracy of 88% and a kappa coefficient of 0.83.

A General Vapor‐Induced Coating Approach for Layer‐controlled Organic Single Crystals

Abstract2D organic semiconductor crystals (2D OSCs) are vital for high‐performance electronic and optoelectronic devices owing to their unique material merits. However, it is still challenging to fabricate high‐quality and large‐scale ultrathin 2D OSCs with controllable molecular layers due to the disordered molecular deposition and uncontrollable mass transport in solution‐processing fabrication. Here, a vapor‐induced meniscus modulating strategy for preparing unidirectional and stable Marangoni flow to guide contactless meniscus evolution is reported, which ensures uniform mass transport and ordered molecular deposition to achieve high‐quality ultrathin 2D OSCs. Both the surface tension difference and the substrate wettability are critical to meniscus formation, which results in various meniscus deformation states and film morphologies. Based on the optimized vapor‐solvent system, ultrathin 2D OSCs of C8‐BTBT with precise layer definition are prepared controllably. The discrepancies in liquid film height and solute concentration are decisive in controlling the molecular scale thickness ranging from mono to a few layers. Moreover, the layer‐dependent electronic and optoelectronic properties of the ultrathin films are systematically investigated. Notably, high‐performance polarization‐sensitive solar‐blind photodetectors are achieved with a dichroic ratio of photocurrent up to 2.26, and the corresponding polarimetric image sensor exhibits superior solar‐blind polarization imaging capability thanks to the high crystalline quality.

A Physically Admissible Stokes Vector Reconstruction in Linear Polarimetric Imaging

A Physically Admissible Stokes Vector Reconstruction in Linear Polarimetric Imaging

Probing Plasma Composition with the Next Generation Event Horizon Telescope (ngEHT)

We explore the plasma matter content in the innermost accretion disk/jet in M87* as relevant for an enthusiastic search for the signatures of anti-matter in the next generation of the Event Horizon Telescope (ngEHT). We model the impact of non-zero positron-to-electron ratio using different emission models, including a constant electron to magnetic pressure (constant βe model) with a population of non-thermal electrons as well as an R-beta model populated with thermal electrons. In the former case, we pick a semi-analytic fit to the force-free region of a general relativistic magnetohydrodynamic (GRMHD) simulation, while in the latter case, we analyze the GRMHD simulations directly. In both cases, positrons are being added at the post-processing level. We generate polarized images and spectra for some of these models and find out that at the radio frequencies, both of the linear and the circular polarizations are enhanced with every pair added. On the contrary, we show that, at higher frequencies, a substantial positron fraction washes out the circular polarization. We report strong degeneracies between different emission models and the positron fraction, though our non-thermal models show more sensitivities to the pair fraction than the thermal models. We conclude that a large theoretical image library is indeed required to fully understand the trends probed in this study, and to place them in the context of a large set of parameters which also affect polarimetric images, such as magnetic field strength, black hole spin, and detailed aspects of the electron temperature and the distribution function.

Semi-Supervised Implicit Neural Representation for Polarimetric ISAR Image Super-Resolution

Compared with the optical imaging system, polarimetric inverse synthetic aperture radar (ISAR) can work all-day and all-weather, which plays an important role in space surveillance. However, high-resolution (HR) ISAR images usually require large bandwidth and coherent integration angle, which is limited by the equipment’s physical conditions. In this vein, the super-resolution (SR) of ISAR images is of vital importance. At present, supervised learning methods are often used in image SR of computer vision. By constructing low-resolution (LR) and HR data pairs, the neural network can learn the mapping relationship between them. However, the low-frequency information in LR image data is less considered. In addition, to obtain different scales of SR reconstruction results, multiple network training repetitions are usually needed, which consumes time and hardware resources. Based on the idea of implicit neural representation, this paper constructs an implicit neural network representation framework for polarimetric ISAR image SR, which can obtain multiscale SR results through one training. A semi-supervised module is also constructed to make the network have the ability of supervised and unsupervised learning, which is conducive to mine and make better use of LR images. A polarimetric ISAR image SR dataset is constructed for satellite targets while four indexes are adopted for quantitative evaluation in global and local aspects. Experiments demonstrate that the proposed approach achieves better SR performance, where the PSNR index can be increased at least by 0.93dB.

Polarization Maintaining 3-D Convolutional Neural Network for Color Polarimetric Images Denoising

Color polarimetric imaging could provide multi-dimensional information that provides physical properties related to object shape, surface roughness, etc. Therefore, it has been widely used in various instrumentation and measurement applications, such as 3D measurement, surface detection, and navigation. However, compared to conventional color images, polarimetric images have a lower signal-to-noise ratio and are more sensitive to noise, which leads to unpleasant noisy images and destroys polarization analysis performance. Moreover, the multiple dimensions and physical relationships among dimensions make the issue of denoise more complicated and challenging. When denoising color polarimetric images, the coherence among the space, color, and polarization is significant but has not been fully exploited by existing methods. In this paper, we propose a three-dimensional (3D) convolutional neural network for denoising color polarimetric images. 3D convolutions are applied to extract information from multiple dimensions, i.e., the space, color, and polarization. In particular, we also found that 3D features derived from color polarimetric images still satisfy the Stokes relationship after 3D convolution. In other words, polarization properties are maintained. Experiments show that the proposed denoising method can well remove noises and restore polarization information in all channels. The proposed denoising method may find important applications for other multi-dimensional imaging tasks.

Complete Mueller matrix imaging polarimeter for evaluating optical components for structured light

We present a complete Mueller matrix (MM) imaging polarimeter based on liquid-crystal retarders and a pixelated polarization camera. The polarimeter instrument is first calibrated and optimized, and then applied for the precise characterization of optical components used for the generation of structured light, like patterned retarders and patterned polarizers.

Three-Dimensional Polarimetric InISAR Imaging of Non-Cooperative Targets

A new Polarimetric Interferometry Inverse Synthetic Aperture Radar (Pol-InISAR) 3D imaging method for non-cooperative targets is proposed in this paper. 3D imaging of non-cooperative targets becomes possible by combining additional information of interferometric phase along with conventional 2D ISAR imaging. In the previously reported single-polarimetry InISAR based 3D imaging, only a single-channel based interferometric phase is available that can be exploited to reconstruct the 3D ISAR image. This limits the ability to obtain a full target's scattering response and therefore limits the estimation of an accurate interferometric phase. To overcome this constraint, full-polarimetry information is being exploited in this paper, which allows to select the optimal polarimetric combination through which the highest coherence can be obtained. A higher coherence leads to a reduction (optimally a minimization) of the phase estimation error. Consequently, with an optimal phase estimation, an accurate 3D imaging of the target is possible. To validate this proposed Pol-InISAR based 3D imaging approach, both simulated and real datasets are taken under consideration.