Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors

• URL: http://arxiv.org/abs/2211.15307v1
• Date: Mon, 28 Nov 2022 13:41:14 GMT
• Title: Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors
• Authors: Xiuheng Wang, Jie Chen, C\'edric Richard
• Abstract summary: We introduce a tuning-free Plug-and-Play (Play) algorithm for HSI deconvolution. Specifically, we use the alternating direction multipliers (ADMM) to decompose the problem into two iterative sub-problems. A flexible blind 3D denoising network (B3DDN) is designed to learn deep priors and to solve the denoising sub-problem with different noise levels.
• Score: 6.0622962428871885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices. This issue is usually addressed by solving an ill-posed inverse problem. While investigating proper image priors can enhance the deconvolution performance, it is not trivial to handcraft a powerful regularizer and to set the regularization parameters. To address these issues, in this paper we introduce a tuning-free Plug-and-Play (PnP) algorithm for HSI deconvolution. Specifically, we use the alternating direction method of multipliers (ADMM) to decompose the optimization problem into two iterative sub-problems. A flexible blind 3D denoising network (B3DDN) is designed to learn deep priors and to solve the denoising sub-problem with different noise levels. A measure of 3D residual whiteness is then investigated to adjust the penalty parameters when solving the quadratic sub-problems, as well as a stopping criterion. Experimental results on both simulated and real-world data with ground-truth demonstrate the superiority of the proposed method.

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