Polarimetric Images Research Articles

Chromatic aberration measurement of liquid crystal Pancharatnam Berry phase lens by a RGB full-Stokes imaging polarimeter

Pancharatnam Berry (PB) phase optical elements can manipulate local polarization state of light in the cross-sectional plane by addressing an additional PB phase relating to the orientation of the optical axis of the waveplate-like structure. However, chromatic aberration including spectrum-dependent retardation and diffraction efficiency limits their applications in the field of visible broadband imaging and display. In this article, the chromatic aberration of a liquid crystal (LC) PB phase lens was measured by a home-built RGB full-Stokes imaging polarimeter. The chirality conversion efficiency and depolarization at RGB wavelengths can be calculated from the measured Stokes vectors. Our experiments show that even the spectrum-dependent retardation exists, the measured PB phase lens has a uniform and high diffraction efficiency in the visible band. Affected by the local LC molecule, the input polarization of RGB light was rotated in varying degrees, but the focusing or defocusing manipulation to the right- or left-hand circular polarization, originated from the PB phase, results in fixed focal lengths for different colors. Our experiments show that the LC-based PB phase lens is suitable for the visible broadband applications.

Reconstruction of total solar irradiance variability as simultaneously apparent from Solar Orbiter and Solar Dynamics Observatory

Solar irradiance variability has been monitored almost exclusively from the Earth’s perspective. We present a method to combine the unprecedented observations of the photospheric magnetic field and continuum intensity from outside the Sun-Earth line, which is being recorded by the Polarimetric and Helioseismic Imager on board the Solar Orbiter mission (SO/PHI), with solar observations recorded from the Earth’s perspective to examine the solar irradiance variability from both perspectives simultaneously. Taking SO/PHI magnetograms and continuum intensity images from the cruise phase of the Solar Orbiter mission and concurrent observations from the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) as input into the SATIRE-S model, we successfully reconstructed the total solar irradiance variability as apparent from both perspectives. In later stages of the SO mission, the orbital plane will tilt in such a way as to bring the spacecraft away from the ecliptic to heliographic latitudes of up to 33°. The current study sets the template for the reconstruction of solar irradiance variability as seen from outside the ecliptic from data that SO/PHI is expected to collect from such positions. Such a reconstruction will be beneficial to factoring inclination into how the brightness variations of the Sun compare to those of other cool stars, whose rotation axes are randomly inclined.

Polarimetric measurement of non-depolarizing optical systems

The use of polarization measurements has become more common in recent years, as it gives more information than pure intensity measurements. Polarimetric components such as fixed or variable retarders and polarizers must be included in optical systems to obtain the polarization parameters required, and in many cases the optical system also includes other components such as relay and/or imaging optical systems. In this work we present a simple and robust method for the polarimetric characterization of non-depolarizing polarization components and other optical elements in the system, which does not require a full polarimeter. Since there is no depolarization, we represent the components as pure retarders with diattenuation and find their parameters (transmittance for the polarization components, angle of orientation of the fast axis, and retardance), from which we can retrieve their Mueller matrix. Our results show that the proposed method is accurate when compared with results obtained with a Mueller matrix dual-rotating retarder polarimeter calibrated using the eigenvalue calibration method, considered in this work as the gold standard, and is comparatively easier than the latter to implement, particularly for imaging polarimeters.

Characterization of black hole accretion through image moment invariants

ABSTRACT We apply image moment invariant analysis to total intensity and polarimetric images calculated from general relativistic magnetohydrodynamic simulations of accreting black holes. We characterize different properties of the models in our library by their invariant distributions and their evolution in time. We show that they are highly sensitive to different physical effects present in the system which allow for model discrimination. We propose a new model scoring method based on image moment invariants that is uniformly applicable to total intensity and polarimetric images simultaneously. The method does not depend on the type of images considered and its application to other non-ring like images (e.g. jets) is straight forward.

Simultaneous broadband and high circular dichroism with two-dimensional all-dielectric chiral metasurface.

Chiral metasurfaces have great potential in various applications such as polarimetric imaging and biomedical recognition. However, simultaneous broadband and high circular dichroism (CD) with high polarization extinction ratio (PER) remains a challenge. Here, we present a novel approach to realize simultaneous broadband and high CD with high PER in the optical communication band using a two-dimensional all-dielectric chiral metasurface. The structure is formed by a two-level chiral structure of split cross (first-order) and trapezoid-shaped (second-order) of Si nano ribs, respectively, in which constructively coupled first- and second-order of chirality occurs, resulting in the broad chiral response in the far field of multipoles excited by incident light of different chiralities. Theoretical results show that a CD in transmission reaching 0.9 (up to 0.993) and a PER exceeding 20 dB (up to 35 dB) over the entire wavelength range from 1.39 to 1.61 μm can be achieved simultaneously, consistent with the experimental results of CD ∼0.9 and PER of 10 dB (up to 19.7 dB). Our design paves the way for chiral metasurfaces toward practical applications in terms of working bandwidth, high CD and PER as well as integrality of the devices in many fields.

Mitigating illumination-, leaf-, and view-angle dependencies in hyperspectral imaging using polarimetry.

Abstract Automation of plant phenotyping using data from high-dimensional imaging sensors is on the forefront of agricultural research for its potential to improve seasonal yield by monitoring crop health and accelerating breeding programs. A common challenge when capturing images in the field relates to the spectral reflection of sunlight (glare) from crop leaves which, at certain solar incidences and sensor viewing angles, presents unwanted signals. The research presented here involves the convergence of two parallel projects to develop an algorithm which can use polarization data to decouple light reflected from the surface of the leaves and light scattered from the leaf's tissue. The first project is a mast-mounted hyperspectral imaging polarimeter (HIP) that can image a maize field across multiple diurnal cycles throughout a growing season. The second project is a multistatic fiber-based (MFB) Mueller matrix bidirectional reflectance distribution function (mmBRDF) instrument which measures the polarized light-scattering behavior of individual maize leaves. This data was fitted to an existing model using SCATMECH, which outputs parameters that were used to run Monte Carlo simulations. The simulated data were then used to train a shallow neural network which works by comparing unpolarized two-band vegetation index (VI) with linearly polarized data from the low-reflectivity bands of the VI. Using GNDVI and Red-edge Reflection Ratio (RERR) we saw an improvement of an order of magnitude or more in the mean error (ε) and a reduction spanning 1.5 to 2.7 in their standard deviation (εσ) after applying the correction network on the HIP sensor data.

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

AbstractClassification with a few samples of training set has been a longstanding issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. Aiming at the small number of training samples of the PolSAR image classification task, a novel Self‐Supervised Ensemble Learning Framework (SSELF) is designed. The designed SSELF can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, utilise the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet‐B0 is presented and utilised as the main section of the Deep Learning (DL) model to extract DL features of PolSAR data. Then, using an optimisation function that constrains the cross‐correlation matrix of various distortions of each sample to the identity matrix, the designed DL model can obtain the effective features of homogeneous samples gathering and heterogeneous samples separating from each other in a self‐supervised manner. Moreover, following the great success of curriculum learning in the area of machine learning, a novel deep curriculum‐learning model is proposed, entitled Deep Curriculum Learning (DCL), to train the DL network in our self‐supervised model. The proposed DCL utilises the entropy‐alpha target decomposition to estimate the degree of complexity of each PolSAR image patch before applying it to the EfficientNet‐B0. Also, an accumulative mini‐batch pacing function is used to introduce more difficult patches to EfficientNet‐B0 in the training process of the designed self‐supervised model. Furthermore, two EL models, feature‐level and view‐level ensemble, are proposed to increase the feature extraction capability and classification result of PolSAR data by jointly using spatial features at different scales and polarimetric information at different bands. In fact, in the proposed feature‐level ensemble strategy, to improve the classification result, the extracted features of the different scales are used as the input of the designed feature fusion algorithm. Therefore, the extracted features are mapped to the new space with lower dimension. In general, in the proposed view‐level ensemble strategy, first, the Mutual Information (MI) between each feature and the other features calculates, and then based‐on the calculated MI, different group of features are selected as various views. Next, the final classification result of the PolSAR image classification is obtained by using the designed majority vote of the result in each view. It should be noted that unlike existing traditional methods, both amplitude and phase information of SAR images to devolve the training process of the proposed model are used. In addition, all of the parameters of the proposed network are developed to a complex domain. In addition, a complex backpropagation algorithm by using the gradient‐based model is used for training the network. Finally, experimental results on three well‐known PolSAR data sets illustrate that the designed SSELF can extract more discriminant features using the designed DL model and can achieve better classification results than the other six novel DL models in terms of small training samples and adequate labelled samples.

Assessment of the Impact of Nanowarming on Microstructure of Cryopreserved Fibroblast-Containing 3D Tissue Models Using Mueller Polarimetry

We studied the impact of two different thawing mechanisms on the microstructure of defrosted cryopreserved 3D tissue models using transmission Mueller microscopy and a statistical analysis of polarimetric images of thin histological sections of defrosted tissue models. The cryopreserved 3D tissue models were thawed by using either a 37 °C water bath or radio-frequency inductive heating with the magnetic nanoparticles embedded into the 3D tissue model during the preparation process. Polarimetric measurements were conducted at 700 nm and the acquired Mueller matrices of the samples were post-processed using the differential decomposition and the statistical analysis of the maps of the azimuth of the optic axis. Our results indicate the sensitivity of polarimetry to the changes in thawed tissue morphology compared to that of reference non-frozen tissue. Thus, Mueller microscopy can be used as a fast complementary technique to the currently accepted gold standard methods for the assessment of the cryopreserved tissue microstructure after thawing.

Texture-fractal analysis of polarimetric images generated by synthetic aperture radar stations.

All currently known methods and methods for the formation of fractalfeatures of polarimetric radar images. Briefly reviewed newtexture-fractal feature – a directional multifractal signature measured morphologically by the iterative covering method. In relation to the analysis of polarimetric images formed by a synthetic aperture radar, a new concept for their processing is proposed, based on the possibility of taking into account the polarization differences of ground-based spatially distributed objects by identifying the multifractal and anisotropic properties of their texture. A variant of interpretation of the obtained results for automatic optimization of further successful solution of specific segmentation (classification) problems underlying surface, detection and recognition of ground objects.

Forgotten treasures in the HST/FOC UV imaging polarimetric archives of active galactic nuclei

Context. Over its 13 yr of operation (1990–2002), the Faint Object Camera (FOC) on board the Hubble Space Telescope (HST) observed 26 individual active galactic nuclei (AGNs) in ultraviolet (UV) imaging polarimetry. However, not all of the observations have been reduced and analyzed or set within a standardized framework. Aims. We plan to reduce and analyze the AGN observations that have been neglected in the FOC archives using a consistent, novel, and open-access reduction pipeline of our own. We then extend the method to the full AGN sample, thus leading to potential discoveries in the near future. Methods. We developed a new pipeline in Python that will be able to reduce all the FOC observations in imaging polarimetry in a homogeneous way. Most of the previously published reduced observations are dispersed throughout the literature, with the range of different analyses and approaches making it difficult to fully interpret the FOC AGN sample. By standardizing the method, we have enabled a coherent comparison among the different observational sets. Results. In this first paper of a series exploring the full HST/FOC AGN sample, we present an exhaustively detailed account of how to properly reduce the observational data. Current progress in cross-correlation functions, convolution kernels, and a sophisticated merging and smoothing of the various polarization filter images, together with precise propagation of errors, has provided state-of-the-art UV polarimetric maps. We compare our new maps to the benchmark AGN case of NGC 1068 and successfully reproduce the main results previously published, while pushing the polarimetric exploration of this AGN futher, thanks to a finer resolution and a higher signal-to-noise ratio (S/N) than previously reported. We also present, for the first time, an optical polarimetric map of the radio-loud AGN IC 5063 and we examine the complex interactions between the AGN outflows and the surrounding interstellar medium (ISM). Conclusions. Thanks to our newly and standardized reduction pipeline, we were able to explore the full HST/FOC AGN sample, starting with observations that had not been previously published (e.g., IC 5063 here). This pipeline will allow us to make a complete atlas of UV polarimetric images of the 26 unique AGNs observed by the FOC, highlighting the importance and necessity of (imaging) polarimeters for the upcoming new generation of 30-m class telescopes.

Coronal voids and their magnetic nature

Context. Extreme ultraviolet (EUV) observations of the quiet solar atmosphere reveal extended regions of weak emission compared to the ambient quiescent corona. The magnetic nature of these coronal features is not well understood. Aims. We study the magnetic properties of the weakly emitting extended regions, which we name coronal voids. In particular, we aim to understand whether these voids result from a reduced heat input into the corona or if they are associated with mainly unipolar and possibly open magnetic fields, similar to coronal holes. Methods. We defined the coronal voids via an intensity threshold of 75% of the mean quiet-Sun (QS) EUV intensity observed by the high-resolution EUV channel (HRIEUV) of the Extreme Ultraviolet Imager on Solar Orbiter. The line-of-sight magnetograms of the same solar region recorded by the High Resolution Telescope of the Polarimetric and Helioseismic Imager allowed us to compare the photospheric magnetic field beneath the coronal voids with that in other parts of the QS. Results. The coronal voids studied here range in size from a few granules to a few supergranules and on average exhibit a reduced intensity of 67% of the mean value of the entire field of view. The magnetic flux density in the photosphere below the voids is 76% (or more) lower than in the surrounding QS. Specifically, the coronal voids show much weaker or no network structures. The detected flux imbalances fall in the range of imbalances found in QS areas of the same size. Conclusions. We conclude that coronal voids form because of locally reduced heating of the corona due to reduced magnetic flux density in the photosphere. This makes them a distinct class of (dark) structure, different from coronal holes.

Textural-Fractal Analysis of Polarimetric Images Generated by Synthetic Aperture Radar

Textural-Fractal Analysis of Polarimetric Images Generated by Synthetic Aperture Radar

Detection of surface cracks on slanted surfaces and through thick irregular insulation layers using polarimetric microwave imaging

Reliable detection of cracks on slanted metal surfaces and through irregular insulation/coating is critical in many inspection applications. Microwave non-destructive testing (NDT) techniques have proven to be effective for crack detection and characterization. However, the utility of conventional microwave NDT techniques for challenging inspection scenarios which involve cracks on slanted surfaces, or cracks under relatively thick, and possibly, irregular insulation has been limited. In this paper, a polarimetric microwave imaging approach is employed for the detection of cracks in such inspection cases. The detection concept using interrogation with dual polarized wave to suppress the background is presented. It is established herein that the cross-polarization measurement is independent to irregular surfaces, and therefore, it constitutes an effective mean for reliable crack detection even if the background is not uniform or if the metal surface is slanted. This fact is further corroborated by numerical electromagnetic simulations and measurements. The efficacy of the proposed method is demonstrated using imaging results of various samples spanning the abovementioned scenarios.

Joint network for specular highlight detection and adversarial generation of specular-free images trained with polarimetric data

Specular highlights in images pose a significant challenge in algorithms for image segmentation, object detection and other image-based decision-making systems. However, most systems ignore this particular scenario and neglect input images with specular highlights instead of mitigating it in the pre-processing stage. In this paper, we leverage deep neural networks and take advantage of the varying illumination information in polarimetric images for synthesizing specular-free images. We propose a multi-domain Specular Highlight Mitigation Generative Adversarial Network (SHMGAN) with self-attention. SHMGAN consists of a single generator–discriminator pair trained simultaneously using polarimetric images. The proposed GAN uses a dynamically generated attention mask based on a specularity segmentation network without requiring additional manual input. The network is able to learn the illumination variation between the four polarimetric images and a pseudo-diffuse image. Once trained, SHMGAN is able to generate specular-free images from a single RGB image as input; without requiring any additional external labels. The proposed network is trained and tested publicly available datasets of real-world images. SHMGAN is able to accurately identify the specularity affected pixels and generates high visual quality images with mitigated specular reflections. The generated images are realistic and have very low noise, distortions and aberrations compared to the existing state-of-the-art methods for specular highlight removal.

Impact of corpus callosum fiber tract crossing on polarimetric images of human brain histological sections: ex vivo studies in transmission configuration.

Imaging Mueller polarimetry is capable to trace in-plane orientation of brain fiber tracts by detecting the optical anisotropy of white matter of healthy brain. Brain tumor cells grow chaotically and destroy this anisotropy. Hence, the drop in scalar retardance values and randomization of the azimuth of the optical axis could serve as the optical marker for brain tumor zone delineation. The presence of underlying crossing fibers can also affect the values of scalar retardance and the azimuth of the optical axis. We studied and analyzed the impact of fiber crossing on the polarimetric images of thin histological sections of brain corpus callosum. We used the transmission Mueller microscope for imaging of two-layered stacks of thin sections of corpus callosum tissue to mimic the overlapping brain fiber tracts with different fiber orientations. The decomposition of the measured Mueller matrices was performed with differential and Lu-Chipman algorithms and completed by the statistical analysis of the maps of scalar retardance, azimuth of the optical axis, and depolarization. Our results indicate the sensitivity of Mueller polarimetry to different spatial arrangement of brain fiber tracts as seen in the maps of scalar retardance and azimuth of optical axis of two-layered stacks of corpus callosum sections The depolarization varies slightly () with the orientation of the optical axes in both corpus callosum stripes, but its value increases by 2.5 to 3 times with the stack thickness. The crossing brain fiber tracts measured in transmission induce the drop in values of scalar retardance and randomization of the azimuth of the optical axis at optical path length of . It suggests that the presence of nerve fibers crossing within the depth of few microns will be also detected in polarimetric maps of brain white matter measured in reflection configuration.

Spatial analysis of polarimetric images to enhance near-surface sampling sensitivity: feasibility in demineralized teeth and other tissue-like media.

Early tooth demineralization may be detectable through spatial analysis of polarized light images as demonstrated in this study. This may also prove useful in the early detection of epithelial tumors that comprise the majority of the cancer burden worldwide. The spatial properties of polarized light images have not been greatly exploited in biomedicine to improve sensitivity to superficial tissue regions; therefore, we investigate the optical sampling depth effects as a function of location in the backscattered polarimetric images. Backscattered linear polarization intensity distributions exhibit four-lobed patterns arising through single-scattering, multiple-scattering, and geometrical effects. These photon pathway dynamics are investigated through experimental imaging of microsphere suspensions along with corroborative computational polarization-sensitive Monte Carlo modeling. The studied sampling depth effects of linear and circular polarization images (explored in a previous study) are then evaluated on normal and demineralized human teeth, which are known to differ in their surface and sub-surface structures. Backscattered linear polarization images exhibit enhanced sensitivity to near-surface properties of media (for example, surface roughness and turbidity) at specific locations within the four-lobed patterns. This yields improved differentiation of two tooth types when spatially selecting image regions in the direction perpendicular to the incident linear polarization vector. Circular polarimetric imaging also yields improved differentiation through spatial selection of regions close to the site of illumination. Improved sensitivity to superficial tissues is achieved through a combination of these linear and circular polarimetric imaging approaches. Heightened sampling sensitivity to tissue microstructure in the surface/near-surface region of turbid tissue-like media and dental tissue is achieved through a judicious spatial selection of specific regions in the resultant co-linear and cross-circular backscattered polarimetric images.

Pol2Pol: self-supervised polarimetric image denoising.

In this Letter, we present a self-supervised method, polarization to polarization (Pol2Pol), for polarimetric image denoising with only one-shot noisy images. First, a polarization generator is proposed to generate training image pairs, which are synthesized from one-shot noisy images by exploiting polarization relationships. Second, the Pol2Pol method is extensible and compatible, and any network that performs well in supervised image denoising tasks can be deployed to Pol2Pol after proper modifications. Experimental results show Pol2Pol outperforms other self-supervised methods and achieves comparable performance to supervised methods.

Real-Time Segmentation of Artificial Targets Using a Dual-Modal Efficient Attention Fusion Network

The fusion of spectral–polarimetric information can improve the autonomous reconnaissance capability of unmanned aerial vehicles (UAVs) in detecting artificial targets. However, the current spectral and polarization imaging systems typically suffer from low image sampling resolution, which can lead to the loss of target information. Most existing segmentation algorithms neglect the similarities and differences between multimodal features, resulting in reduced accuracy and robustness of the algorithms. To address these challenges, a real-time spectral–polarimetric segmentation algorithm for artificial targets based on an efficient attention fusion network, called ESPFNet (efficient spectral–polarimetric fusion network) is proposed. The network employs a coordination attention bimodal fusion (CABF) module and a complex atrous spatial pyramid pooling (CASPP) module to fuse and enhance low-level and high-level features at different scales from the spectral feature images and the polarization encoded images, effectively achieving the segmentation of artificial targets. Additionally, the introduction of the residual dense block (RDB) module refines feature extraction, further enhancing the network’s ability to classify pixels. In order to test the algorithm’s performance, a spectral–polarimetric image dataset of artificial targets, named SPIAO (spectral–polarimetric image of artificial objects) is constructed, which contains various camouflaged nets and camouflaged plates with different properties. The experimental results on the SPIAO dataset demonstrate that the proposed method accurately detects the artificial targets, achieving a mean intersection-over-union (MIoU) of 80.4%, a mean pixel accuracy (MPA) of 88.1%, and a detection rate of 27.5 frames per second, meeting the real-time requirement. The research has the potential to provide a new multimodal detection technique for enabling autonomous reconnaissance by UAVs in complex scenes.

Chip-integrated metasurface full-Stokes polarimetric imaging sensor

Polarimetric imaging has a wide range of applications for uncovering features invisible to human eyes and conventional imaging sensors. Chip-integrated, fast, cost-effective, and accurate full-Stokes polarimetric imaging sensors are highly desirable in many applications, which, however, remain elusive due to fundamental material limitations. Here we present a chip-integrated Metasurface-based Full-Stokes Polarimetric Imaging sensor (MetaPolarIm) realized by integrating an ultrathin (~600 nm) metasurface polarization filter array (MPFA) onto a visible imaging sensor with CMOS compatible fabrication processes. The MPFA is featured with broadband dielectric-metal hybrid chiral metasurfaces and double-layer nanograting polarizers. This chip-integrated polarimetric imaging sensor enables single-shot full-Stokes imaging (speed limited by the CMOS imager) with the most compact form factor, records high measurement accuracy, dual-color operation (green and red) and a field of view up to 40 degrees. MetaPolarIm holds great promise to enable transformative applications in autonomous vision, industry inspection, space exploration, medical imaging and diagnosis.

An Image Classification Method Based on Semi-Supervised Classification Learning and Convolutional Neural Networks

This paper aims to propose an improved image classification model to reduce the cost of model construction. Aiming at the problem that network training usually requires the support of a large number of labeled samples, an image classification model based on semi-supervised deep learning is proposed, which uses labeled samples to guide the network to learn unlabeled samples. A convolutional neural network model for simultaneous processing of labeled and unlabeled data is constructed. The tagged data is used to train the Softmax classifier and provide the initial K-means clustering center for the untagged data. The nonsubsampling contourlet layer is used to replace the first convolutional layer of the full convolutional neural network to extract multi-scale depth features, and the nonsubsampling contourlet full convolutional neural network is constructed. The network can extract multi-scale information of the images to be classified, and extract more discriminative deep image features. In addition, the parameters of the nonsubsampled contourlet layers are pre-set and do not require network training. The proposed method has higher classification accuracy than the contrast method on polarimetric SAR images using the nonsubsampled contourlet full convolutional neural network.