Channeled compressive imaging spectropolarimeter

Joint constraints of guided filtering based confidence and nonlocal sparse tensor for color polarization super-resolution imaging

This paper introduces a camera-array-based super-resolution color polarization imaging system designed to simultaneously capture color and polarization information of a scene in a single shot. Existing snapshot color polarization imaging has a complex structure and limited generalizability, which are overcome by the proposed system. In addition, a novel reconstruction algorithm is designed to exploit the complementarity and correlation between the twelve channels in acquired color polarization images for simultaneous super-resolution (SR) imaging and denoising. We propose a confidence-guided SR reconstruction algorithm based on guided filtering to enhance the constraint capability of the observed data. Additionally, by introducing adaptive parameters, we effectively balance the data fidelity constraint and the regularization constraint of nonlocal sparse tensor. Simulations were conducted to compare the proposed system with a color polarization camera. The results show that color polarization images generated by the proposed system and algorithm outperform those obtained from the color polarization camera and the state-of-the-art color polarization demosaicking algorithms. Moreover, the proposed algorithm also outperforms state-of-the-art SR algorithms based on deep learning. To evaluate the applicability of the proposed imaging system and reconstruction algorithm in practice, a prototype was constructed for color polarization image acquisition. Compared with conventional acquisition, the proposed solution demonstrates a significant improvement in the reconstructed color polarization images.

Full-Stokes polarization multispectral images of various stereoscopic objects

Polarization multispectral imaging (PMI) has been applied widely with the ability of characterizing physicochemical properties of objects. However, traditional PMI relies on scanning each domain, which is time-consuming and occupies vast storage resources. Therefore, it is imperative to develop advanced PMI methods to facilitate real-time and cost-effective applications. In addition, PMI development is inseparable from preliminary simulations based on full-Stokes polarization multispectral images (FSPMI). Whereas, FSPMI measurements are always necessary due to the lack of relevant databases, which is extremely complex and severely limits PMI development. In this paper, we therefore publicize abundant FSPMI with 512 × 512 spatial pixels measured by an established system for 67 stereoscopic objects. In the system, a quarter-wave plate and a linear polarizer are rotated to modulate polarization information, while bandpass filters are switched to modulate spectral information. The required FSPMI are finally calculated from designed 5 polarization modulation and 18 spectral modulation. The publicly available FSPMI database may have the potential to greatly promote PMI development and application.

Compressive space-dimensional dual-coded hyperspectral polarimeter (CSDHP) and interactive design method

A compressive space-dimensional dual-coded hyperspectral polarimeter (CSDHP) and interactive design method are introduced. A digital micromirror device (DMD), a micro polarizer array detector (MPA), and a prism grating prism (PGP) are combined to achieve single-shot hyperspectral polarization imaging. The longitudinal chromatic aberration (LCA) and spectral smile of the system are both eliminated to guarantee the matching accuracy of DMD and MPA pixels. A 4D data cube with 100 channels and 3 Stocks parameters is reconstructed in the experiment. The feasibility and fidelity are verified from the image and spectral reconstruction evaluations. It is demonstrated that the target material can be distinguished by CSDHP.

Backtracking Reconstruction Network for Three-Dimensional Compressed Hyperspectral Imaging

Compressed sensing (CS) has been widely used in hyperspectral (HS) imaging to obtain hyperspectral data at a sub-Nyquist sampling rate, lifting the efficiency of data acquisition. Yet, reconstructing the acquired HS data via iterative algorithms is time consuming, which hinders the real-time application of compressed HS imaging. To alleviate this problem, this paper makes the first attempt to adopt convolutional neural networks (CNNs) to reconstruct three-dimensional compressed HS data by backtracking the entire imaging process, leading to a simple yet effective network, dubbed the backtracking reconstruction network (BTR-Net). Concretely, we leverage the divide-and-conquer method to divide the imaging process based on coded aperture tunable filter (CATF) spectral imager into steps, and build a subnetwork for each step to specialize in its reverse process. Consequently, BTR-Net introduces multiple built-in networks which performs spatial initialization, spatial enhancement, spectral initialization and spatial–spectral enhancement in an independent and sequential manner. Extensive experiments show that BTR-Net can reconstruct compressed HS data quickly and accurately, which outperforms leading iterative algorithms both quantitatively and visually, while having superior resistance to noise.

Deep learning Mueller matrix feature retrieval from a snapshot Stokes image

A Mueller matrix (MM) provides a comprehensive representation of the polarization properties of a complex medium and encodes very rich information on the macro- and microstructural features. Histopathological features can be characterized by polarization parameters derived from MM. However, a MM must be derived from at least four Stokes vectors corresponding to four different incident polarization states, which makes the qualities of MM very sensitive to small changes in the imaging system or the sample during the exposures, such as fluctuations in illumination light and co-registration of polarization component images. In this work, we use a deep learning approach to retrieve MM-based specific polarimetry basis parameters (PBPs) from a snapshot Stokes vector. This data post-processing method is capable of eliminating errors introduced by multi-exposure, as well as reducing the imaging time and hardware complexity. It shows the potential for accurate MM imaging on dynamic samples or in unstable environments. The translation model is designed based on generative adversarial network with customized loss functions. The effectiveness of the approach was demonstrated on liver and breast tissue slices and blood smears. Finally, we evaluated the performance by quantitative similarity assessment methods in both pixel and image levels.

Off-axis aberration correction for a reflective coded aperture snapshot spectral imager

The reflective optical configuration of the coded aperture snapshot spectral imager (CASSI) based on a digital micromirror device (DMD) features both high quality and high flexibility of intensity modulation, but the image quality is always subject to off-axis aberration. This letter analyzes the formation of the off-axis aberration, and then proposes an optical-axis-shift (OAS) configuration to compensate for this adverse effect in the DMD-based reflective CASSI system. Experimental results demonstrate that the proposed design can effectively eliminate the off-axis aberration. The average peak signal-to-noise ratio of the reconstructed spectral images is improved by approximately 2.8 dB. In addition, other methods to alleviate the off-axis aberration are discussed, and the superiority of the proposed design is signified. The proposed OAS design is easy to implement with commercially available components, and provides an initial configuration of an optical design to implement a high-performance CASSI instrument.

Channeled imaging spectropolarimeter reconstruction by neural networks

Snapshot channeled imaging spectropolarimetry (SCISP), which can achieve spectral and polarization imaging without scanning (a single exposure), is a promising optical technique. As Fourier transform is used to reconstruct information, SCISP has its inherent limitations such as channel crosstalk, resolution and accuracy drop, the complex phase calibration, et al. To overcome these drawbacks, a nonlinear technique based on neural networks (NNs) is introduced to replace the role of Fourier reconstruction. Herein, abundant spectral and polarization datasets were built through specially designed generators. The established NNs can effectively learn the forward conversion procedure through minimizing a loss function, subsequently enabling a stable output containing spectral, polarization, and spatial information. The utility and reliability of the proposed technique is confirmed by experiments, which are proved to maintain high spectral and polarization accuracy.

Snapshot spectroscopic Mueller matrix polarimetry based on spectral modulation with increased channel bandwidth

This paper presents a snapshot spectroscopic Mueller matrix polarimetry based on spectral modulation. The polarization state generator consists of a linear polarizer in front of two high-order retarders, and the polarization state analyzer is formed by two non-polarization beam splitters incorporated with three high-order retarder/linear analyzer pairs. It can simultaneously generate three modulated spectra used for reconstructing the 16 spectroscopic Mueller elements of the sample. Since each of the modulated spectra produces seven separate channels equally spaced in the Fourier domain, the channel bandwidth can be enhanced efficiently compared with the conventional spectrally modulated spectroscopic Mueller matrix polarimetry. The feasibility of the proposed spectroscopic Mueller matrix polarimetry is demonstrated by the experimental measurement of an achromatic quarter-wave plate.

Snapshot polarized light scattering spectroscopy using spectrally-modulated polarimetry for early gastric cancer detection

Polarized light scattering spectroscopy (PLSS) is a promising optical technique developed for the detection of cancer, which extracts the single scattering light to infer morphological information of epithelial cells. However, traditional PLSS uses either a rotatable polarizer or two orthogonal polarizers to purify the single scattering light, which makes it complicated and challenged to build a PLSS endoscope. Herein, we propose a snapshot PLSS with a single optical path to directly get the single scattering light for the first time. The single scattering light is encoded using the spectrally-modulated polarimetry and decoded using the continuous slide iterative method. Both the polystyrene microsphere solutions and the ex vivo gastric cancer samples are used to verify the method. The experimental results of the snapshot PLSS are consistent well with that of the traditional PLSS. The proposed method has a potential for the building of snapshot PLSS endoscope systems in future.

Spatially modulated snapshot computed tomographic polarization imaging spectrometer

Herein, a spatially modulated snapshot computed tomographic polarization imaging spectrometer (SMSCTPIS) is proposed. This instrument can obtain spatial, intensity, and polarization information of different wavelengths of a target simultaneously. It can also alleviate certain limitations of the spatially modulated snapshot imaging polarimeter (SMSIP) based on a Savart polarizer, which cannot obtain information of different wavelengths in a single measurement. Further, it can also mitigate the need to frequently replace the filters of SMSIPs for different detection wavelengths. The paper introduces the structure and principle of the SMCTPIS first, followed by experiments confirming its accuracy. Finally, the experimental results are analyzed, and conclusions are drawn.

Accurate reconstruction of polarization parameters for channeled spectroscopic Stokes polarimeters

In this work, we present an accurate polarization reconstruction method based on the coherence demodulation technique, which is different from the previous windowing method operating in the optical path difference domain. The proposed method uses a signal multiplier and a low-pass filter to reconstruct Stokes parameters without performing any Fourier transform. Because this method does not require a Fourier transform, the Stokes reconstruction could be finished in the spectral domain. For calibrating the waveplate phase error, coherence demodulation allows for establishing an analytical model to describe the influence of waveplate imperfections on the polarization measurement process. The phase error will result in a channel shift and Fourier broadening, both of which cause serious errors during Stokes reconstruction. With the model, a method based on a linear polarizer was proposed for calibrating the phase deviation of waveplate. After that, the accurate reconstruction of polarization parameters could be achieved. An experiment was performed to check the ability of the proposed method. The experimental result showed that it has the same excellent performance of reconstructing Stokes parameters using the traditional windowing method. Finally, a series of simulations was carried out to verify the robustness of this method, which showed that the reconstruction technique is robust to misalignment and additional noise.

Scaling-Based Two-Step Reconstruction in Full Polarization-Compressed Hyperspectral Imaging

Polarized hyperspectral images can reflect the rich physicochemical characteristics of targets. Meanwhile, the contained plentiful information also brings great challenges to signal processing. Although compressive sensing theory provides a good idea for image processing, the simplified compression imaging system has difficulty in reconstructing full polarization information. Focused on this problem, we propose a two-step reconstruction method to handle polarization characteristics of different scales progressively. This paper uses a quarter-wave plate and a liquid crystal tunable filter to achieve full polarization compression and hyperspectral imaging. According to their numerical features, the Stokes parameters and their modulation coefficients are simultaneously scaled. The first Stokes parameter is reconstructed in the first step based on compressive sensing. Then, the last three Stokes parameters with similar order of magnitude are reconstructed in the second step based on previous results. The simulation results show that the two-step reconstruction method improves the reconstruction accuracy by 7.6 dB for the parameters that failed to be reconstructed by the non-optimized method, and reduces the reconstruction time by 8.25 h without losing the high accuracy obtained by the current optimization method. This feature scaling method provides a reference for the fast and high-quality reconstruction of physical quantities with obvious numerical differences.

Spectroscopic Mueller matrix polarimeter based on spectro-temporal modulation

A spectroscopic Mueller matrix polarimeter based on spectro-temporal modulation with a compact, low-cost, and birefringent crystal-based configuration has been developed. The polarization state generator and polarization state analyzer in the system consists of a polarizer in front of two high-order retarders with equal thickness and a rotating achromatic quarter wave-plate followed by a fixed analyzer, respectively. It can acquire the 16 spectroscopic elements of the Mueller matrix in broadband with a faster measurement speed than that of the conventional spectroscopic Mueller matrix polarimeter based on a dual-rotating retarder. In addition, the spectral polarization modulation provided by the polarization state generator can produce five separate channels in the Fourier domain, which leads to a larger bandwidth of each channel than that of the existing spectral modulated spectroscopic Mueller matrix polarimeters. Experiment on the measurements of an achromatic quarter-wave plate oriented at different azimuths and SiO₂ thin films deposited on silicon wafers with different thicknesses are carried out to show the feasibility of the developed spectroscopic Mueller matrix polarimeter.

Spectral-temporal hybrid modulation for channeled spectropolarimetry

Channeled spectropolarimeters (CSPs) are capable of estimating spectrally resolved Stokes parameters from a single modulated spectrum. However, channel crosstalk and subsequent spectral resolution loss reduce the reconstruction accuracy and limit the systems' scope of application. In this paper, we propose a spectral-temporal modulation strategy with the aim of extending channel bandwidth and improving reconstruction accuracy by leveraging the hybrid carriers and allocating channels in the two-dimensional Fourier domain that yield optimal performance. The scheme enables spectral bandwidth and temporal bandwidth to be traded off, and provides flexibility in selecting demodulation strategies based on the features of the input. We present an in-depth comparison of different systems' performances in various input features under the presence of noise. Simulation results show that the hybrid-modulation strategy offers the best comprehensive performance as compared to the conventional CSP and dual-scan techniques.