Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors
This addresses the problem of blur and noise in hyperspectral images for researchers and practitioners, but it is incremental as it builds on existing Plug-and-Play methods with tuning-free improvements.
The paper tackled hyperspectral image deconvolution by introducing a tuning-free Plug-and-Play algorithm that uses a deep prior network and adaptive parameter adjustment, achieving superior results on simulated and real-world data with ground-truth.
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.