Tuning-Free Plug-and-Play Hyperspectral Image Deconvolution With Deep Priors

Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images (HSIs) 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 article, 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 subproblems. A flexible blind 3-D denoising network (B3DDN) is designed to learn deep priors and to solve the denoising subproblem with different noise levels. A measure of 3-D residual whiteness is then investigated to adjust the penalty parameters when solving the quadratic subproblems, 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.