Compressive Spectral Imaging Via Virtual Side Information

Compressive spectral imaging (CSI) allows the acquisition of the spectral information of a three-dimensional scenes by using coded projections in a sensor with lower dimension. However, the compressed sampling of information with simultaneously high spatial and high spectral resolution demands expensive high-resolution sensors. One of the main challenges in CSI is to obtain a high-quality image of high-resolution reconstructions using low-cost architectures. Single pixel camera is an approach that has had a high impact in spectroscopy, due to its low-cost implementation compared to CSI architectures with 2D sensors. On the other hand, recent works have been shown that image fusion using measurements from a CSI sensor based on side information leads to improvement in the quality of the fused image. This work proposes a spectral image fusion methodology for increasing the spatio-spectral resolution through side information and at the same time improve the reconstruction quality of the data cube with a low-cost architecture, optimizing the similarity of the reconstructed spectral image with each sensor. Simulations and experimental results for the proposed methodology show that improve the quality of the reconstruction in up to 11 dB with respect to the traditional approach of upsampling the single pixel image reconstruction through bilinear interpolation.

LADMM-Net: An unrolled deep network for spectral image fusion from compressive data

Compressive spectral imaging (CSI) enables the acquisition of spectral and spatial information of a scene using fewer projected measurements than traditional scanning approaches. Recently, research efforts have focused on obtaining high-resolution spectral images via expensive detectors and sophisticated CSI devices. Alternatively, high-resolution spectral images can be obtained using side information or fusion of compressed measurements, without significantly increasing acquisition costs. Indeed, these approaches retrieve improved resolution images applying iterative and computationally expensive algorithms. This paper proposes the fusion of compressed measurements obtained from two state-of-the-art CSI systems, the single-pixel camera (SPC) and the three-dimensional coded aperture snapshot imaging system (3D-CASSI), such that high-resolution images can be obtained by exploiting detailed spectra provided by the SPC and high spatial resolution of the 3D-CASSI. Specifically, a noniterative reconstruction algorithm is proposed, based on the fact that the spatial-spectral data lie in a low-dimensional subspace. In contrast to related works, the proposed approach relies on implementable CSI systems. Simulations and experimental results show the effectiveness of the proposed method compared to similar approaches, both in reconstruction quality and complexity. Specifically, the proposed method is up to 5.6 times faster than its counterparts and provides comparable quality of attained reconstructions.

Compressive spectral imaging (CSI) acquires compressed observations of a spectral scene by applying different coding patterns at each spatial location and then performing a spectral-wise integration. Relying on compressive sensing, spectral image reconstruction is achieved by using nonlinear and relatively expensive optimization-based algorithms. In the CSI literature, several works have focused on improving reconstructions quality by properly designing the set of coding patterns. However, signal recovery is not actually necessary in many signal processing applications. For instance, assuming that compressed measurements with similar characteristics lie on the same subspace, unsupervised methods such as subspace clustering can be used to separate them into the same cluster. Since the structure of compressed measurements is defined by the applied codification, it is possible to improve clustering performance. This paper proposes to design a set of coding patterns such that inter-class and intra-class data structure is preserved after the CSI acquisition in order to improve clustering results directly on the compressed domain. To validate the coding pattern design, an algorithm based on sparse subspace clustering (SSC) is proposed to perform clustering on the compressed measurements. The proposed algorithm adds a three-dimensional (3-D) spatial regularizer to the SSC problem exploiting the spatial correlation of spectral images. In general, an overall accuracy up to 83.81% is obtained, when noisy measurements are assumed. In addition, a difference of at most 4% in terms of overall accuracy was observed when comparing the clustering results obtained by the full 3-D data with those achieved using CSI measurements acquired with the proposed coding pattern design.

Widely used MRI methods show brain morphology both in vivo and ex vivo at very high resolution. Many of these methods (e.g., T2*-weighted imaging, phase-sensitive imaging, or susceptibility-weighted imaging) are sensitive to local magnetic susceptibility gradients produced by subtle variations in tissue composition. However, the spectral resolution of commonly used methods is limited to maintain reasonable run-time combined with very high spatial resolution. Here, the authors report on data acquisition at increased spectral resolution, with 3-dimensional high spectral and spatial resolution MRI, in order to analyze subtle variations in water proton resonance frequency and lineshape that reflect local anatomy. The resulting information compliments previous studies based on T2* and resonance frequency. The proton free induction decay was sampled at high resolution and Fourier transformed to produce a high-resolution water spectrum for each image voxel in a 3D volume. Data were acquired using a multigradient echo pulse sequence (i.e., echo-planar spectroscopic imaging) with a spatial resolution of 50 × 50 × 70 μm(3) and spectral resolution of 3.5 Hz. Data were analyzed in the spectral domain, and images were produced from the various Fourier components of the water resonance. This allowed precise measurement of local variations in water resonance frequency and lineshape, at the expense of significantly increased run time (16-24 h). High contrast T2*-weighted images were produced from the peak of the water resonance (peak height image), revealing a high degree of anatomical detail, specifically in the hippocampus and cerebellum. In images produced from Fourier components of the water resonance at -7.0 Hz from the peak, the contrast between deep white matter tracts and the surrounding tissue is the reverse of the contrast in water peak height images. This indicates the presence of a shoulder in the water resonance that is not present at +7.0 Hz and may be specific to white matter anatomy. Moreover, a frequency shift of 6.76 ± 0.55 Hz was measured between the molecular and granular layers of the cerebellum. This shift is demonstrated in corresponding spectra; water peaks from voxels in the molecular and granular layers are consistently 2 bins apart (7.0 Hz, as dictated by the spectral resolution) from one another. High spectral and spatial resolution MR imaging has the potential to accurately measure the changes in the water resonance in small voxels. This information can guide optimization and interpretation of more commonly used, more rapid imaging methods that depend on image contrast produced by local susceptibility gradients. In addition, with improved sampling methods, high spectral and spatial resolution data could be acquired in reasonable run times, and used for in vivo scans to increase sensitivity to variations in local susceptibility.

Conventional multispectral imaging systems based on bandpass filters struggle to record multispectral videos with high spatial resolutions because of their limited light efficiencies. This paper proposes a multi-aperture multispectral imaging system based on notch filters that overcomes this limitation by allowing light from most of the spectrum to pass through. Based on this imaging principle, a prototype multi-aperture multispectral imaging system comprising notch filters was built and demonstrated. Further, a dictionary learning- and total variation-based spectral super-resolution algorithm was developed to reconstruct spectral images. The simulation results obtained using public multispectral datasets showed that, compared to the dictionary learning-based spectral super-resolution algorithm, the proposed algorithm reconstructed the spectral information with a higher accuracy and removed noise, and the verification experiments confirmed the performance efficiency of the prototype system. The experimental results showed that the proposed imaging system can capture images with high spatial and spectral resolutions under low illumination conditions. The proposed algorithm improved the spectral resolution of the acquired data from 9 to 31 bands, and the average peak signal-to-noise ratio remained above 43 dB, which is 13 dB higher than those of the state-of-the-art coded aperture snapshot spectral imaging methods. Simultaneously, the frame rate of the imaging system was up to 5000 frames/s under natural daylight.

Compressive spectral imaging has garnered significant attention for its ability to effectively enhance the captured spatial and spectral information. Predominant methods, based on compressive sensing, typically formulate the imaging task as a constrained optimization problem and rely on hand-crafted priors to model the sparsity of spectral images. However, these approaches often suffer from suboptimal performance due to the inherent difficulty of identifying an appropriate transform space where spectral images exhibit sparsity. To overcome this limitation, we propose a novel convolutional sparse coding-inspired untrained network prior for fast and adaptive identification of the sparse transform domain and compressible signal. Specifically, a Lightweight Convolutional Thresholding sparse Coding (LCTC) network is designed as the sparse transform domain, with its inputs interpreted as sparse coefficients. Crucially, both the transform domain and its coefficients are solved in a self-supervised learning manner. Furthermore, we demonstrate that LCTC prior can be seamlessly incorporated into the iterative optimization algorithm as a Plug-and-Play (PnP) regularization. Both the LCTC and PnP-LCTC exhibit superior performance compared to previous methods. Experiments under various scenarios validate the effectiveness and efficiency of our approach.

Compressive spectral imaging (CSI) acquires coded projections of a spectral image by performing a modulation of the data cube followed by a spectral-wise integration. To avoid the spectral image reconstruction procedure, this paper proposes a classification approach that extracts features directly from multi-sensor CSI measurements. Particularly, the proposed method obtains the features by considering the spectral information extracted from Hyperspectral CSI measurements, and the local spatial information extracted by clustering the Multispectral CSI measurements using a superpixel algorithm. This approach is evaluated on Pavia University and Salinas Valley datasets. Extensive simulations show that considering the local spatial information boosts the overall accuracy up to 3% in comparison with traditional approaches that only uses the spectral information. Furthermore, the computation time of the approach that reconstructs, fuses and classifies takes approximately 87.43 [s], while classifying directly from multi-sensor compressive measurements takes only 0.74 [s], achieving similar classification results.

DMP-Net: Deep semantic prior compressed spectral reconstruction method towards intraoperative imaging of brain tissue.

Coded aperture snapshot spectral imager (CASSI), efficiently captures 3D spectral images by sensing 2D projections of the scene. While CASSI offers a substantial reduction in acquisition time, compared to traditional scanning optical systems, it requires a reconstruction post-processing step. Furthermore, to obtain high-quality reconstructions, an accurate propagation model is required. Notably, CASSI exhibits a variant spatio-spectral sensor response, making it difficult to acquire an accurate propagation model. To address these inherent limitations, this work proposes to learn a deep non-linear fully differentiable propagation model that can be used as a regularizer within an optimization-based reconstruction algorithm. The proposed approach trains the non-linear spatially-variant propagation model using paired compressed measurements and spectral images, by employing side information only in the calibration step. From the deep propagation model incorporation into a plug-and-play alternating direction method of multipliers framework, our proposed method outperforms traditional CASSI linear-based models. Extensive simulations and a testbed implementation validate the efficacy of the proposed methodology.

Spectral imaging combines the advantages of spectroscopy as an analytical tool with the two-dimensional object visualization obtained by optical imaging. At present, spectral imaging systems suffer from severe scale-dependent geometric aberrations. This prevents the simultaneous realization of a wide field of view (FOV) and high resolution. Here we propose an ultra-wide-FOV high-resolution cascaded spectral imaging system (URIS), which captures an ultra-wide-FOV panchromatic image with a folding monocentric lens and obtains high-resolution spectral images after local aberration correction by the spectral imaging sub-channels. The key issue of how the position of the aperture stop affects the illumination uniformity of the sub-channels has been studied, contributing to achieving the spectral images stitching between adjacent sub-channels and improving the performance of the processed spectral image. The URIS is optimally designed with 19 spectral imaging sub-channels to cover a full FOV of about 110°. It achieves an excellent spatial resolution of 0.075 mrad and a high spectral resolution of 4 nm theoretically. An experiment is also demonstrated to verify the wide FOV and the uniformity of sub-channels of URIS, confirming the correctness of the theoretical study. URIS has great advantages in spectral imaging of large macroscopic scenes, inspiring various machine vision applications.

Massive amounts of data in spectral imagery increase acquisition, storing and processing costs. Compressive spectral imaging (CSI) methods allow the reconstruction of spatial and spectral information from a small set of random projections. The single pixel camera is a low cost optical architecture which enables the compressive acquisition of spectral images. Traditional CSI reconstruction methods obtain a sparse approximation of the underlying spatial and spectral information, however the complexity of these algorithms increases in proportion to the dimensionality of the data. This work proposes a multiresolution (MR) CSI reconstruction approach from single pixel camera measurements that exploits spectral similarities between pixels to group them in super-pixels such that the total number of unknowns in the inverse problem is reduced. Specifically, two different types of super-pixels are considered: rectangular and irregular structures. Simulation and experimental results show that the proposed MR scheme improves reconstruction quality in up to 6dB of PSNR and reconstruction time in up to 90% with respect to the traditional full resolution reconstructions.

Spectral imaging is of interest in many applications, including wide-area airborne surveillance, remote sensing, and tissue spectroscopy. Coded aperture spectral snapshot imaging (CASSI) provides an efficient mechanism to capture a 3D spectral cube with a single shot 2D measurement. CASSI uses a focal plane array (FPA) measurement of a spectrally dispersed, aperture coded, source. The spectral cube is then attained using a compressive sensing reconstruction algorithm. In this paper, we explore a new approach referred to as random convolution snapshot spectral imaging (RCSSI). It is based on FPA measurements of spectrally dispersed coherent sources that have been randomly convoluted by a spatial light modulator. The new method, based on the theory of compressive sensing via random convolutions, is shown to outperform traditional CASSI systems in terms of PSNR spectral image cube reconstructions.

Coded Aperture Snapshot Spectral Imaging (CASSI) is an effective tool to capture spectral images, which has the advantages of snapshot imaging, high luminous flux, high signal-to-noise ratio and low sampling frequency. However, conventional CASSI generally uses refractive prisms or gratings for spectral dispersion, which leads to the nonlinear dispersion phenomenon and the requirement of large detector chip respectively. To overcome these issues, conventional refractive prisms or gratings are replaced by an axially dispersive diffractive optical element (DOE, i.e., computational optics) together with a RGB Bayer filter (i.e., a color-coded aperture) in this study. Specifically, the spatial-spectral information of a test scene is jointly modulated by the DOE and the Bayer filter integrated with a sensor chip. A fully differentiable imaging model is built based on the principle of diffractive optics and the deep learning technology. Furthermore, an optimization design of the DOE with the coded aperture is realized through an end-to-end approach, the output spectral images of which are restored by a Res-Unet neural network. Several simulation results show that up to 31 high-fidelity spectral bands in the range of 400 to 700 nm with a good spatial and spectral resolution can be recovered by the proposed snapshot system.

A spectral imager constructs a three-dimensional (two spatial and one spectral) image from a series of two-dimensional images. We discuss a technique for spectral imaging that multiplexes the spatial and spectral information on a staring focal plane and then demultiplexes the resulting imagery to obtain the spectral image. The spectral image consists of 100×100 spatial pixels and 25 spectral bands. The current implementation operates over the 3–5-μm band, but can easily be applied to other spectral regions. This approach to spectral imaging has high optical throughput and is robust to focal plane array nonuniformities. A hardware description, the mathematical development, and experimental results are presented.