A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors

• URL: http://arxiv.org/abs/2206.05647v1
• Date: Sun, 12 Jun 2022 03:29:14 GMT
• Title: A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors
• Authors: Qile Zhao, Xianhong Zhao, Xu Ma, Xudong Chen, Gonzalo R. Arce
• Abstract summary: Coded aperture snapshot spectral imaging (CASSI) is a technique used to reconstruct three-dimensional hyperspectral images (HSIs) This paper proposes a fast alternating minimization algorithm based on the sparsity and deep image priors (Fama-P) of natural images.
• Score: 8.890754092562918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coded aperture snapshot spectral imaging (CASSI) is a technique used to reconstruct three-dimensional hyperspectral images (HSIs) from one or several two-dimensional projection measurements. However, fewer projection measurements or more spectral channels leads to a severly ill-posed problem, in which case regularization methods have to be applied. In order to significantly improve the accuracy of reconstruction, this paper proposes a fast alternating minimization algorithm based on the sparsity and deep image priors (Fama-SDIP) of natural images. By integrating deep image prior (DIP) into the principle of compressive sensing (CS) reconstruction, the proposed algorithm can achieve state-of-the-art results without any training dataset. Extensive experiments show that Fama-SDIP method significantly outperforms prevailing leading methods on simulation and real HSI datasets.

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