We review the recent deep learning reconstruction algorithms for spectral snapshot compressive imaging (SCI), which used a single shot measurement to capture the three-dimensional (3D, x, y, λ) spectral image. Recent years, deep learning has been the dominant algorithm to conduct reconstruction due to high speed and accuracy. Various frameworks such as end-to-end neural networks, deep unfolding, plug-and-play networks have been developed. Furthermore, the untrained neural networks have also been used. In this paper, we review diverse deep learning methods for spectral SCI. In addition to the aforementioned frameworks, different backbones and network structures including the most recent Transformers are reviewed. Simulation and real data results are presented to compare these methods.
We propose a novel joint compressive imaging system, which combines the merit of Single Pixel Camera (SPC) and Coded Aperture Snapshot Spectral Imaging (CASSI) system. This enables us to capture multi- or hyperspectral information with a single pixel detector. The desired 3D image cube is reconstructed by a concatenation of deep-unfolding-based algorithm and plug-and-play algorithm with deep-learning-based denoiser. We demonstrate the feasibility of the proposed system in both simulation and experiments. With advanced algorithms, the joint compressive imaging system is able to output comparable hyperspectral images with existing SD-CASSI system. Moreover, by adapting ultra-broad-spectrum photodiodes, the proposed system can be easily expanded to Near- and Mid-infrared band and thus being a low-cost approach to IR spectroscopy.
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