Deep Plug-and-Play Prior for Hyperspectral Image Restoration
arXiv:2209.08240 · doi:10.1016/j.neucom.2022.01.057
Abstract
Deep-learning-based hyperspectral image (HSI) restoration methods have gained great popularity for their remarkable performance but often demand expensive network retraining whenever the specifics of task changes. In this paper, we propose to restore HSIs in a unified approach with an effective plug-and-play method, which can jointly retain the flexibility of optimization-based methods and utilize the powerful representation capability of deep neural networks. Specifically, we first develop a new deep HSI denoiser leveraging gated recurrent convolution units, short- and long-term skip connections, and an augmented noise level map to better exploit the abundant spatio-spectral information within HSIs. It, therefore, leads to the state-of-the-art performance on HSI denoising under both Gaussian and complex noise settings. Then, the proposed denoiser is inserted into the plug-and-play framework as a powerful implicit HSI prior to tackle various HSI restoration tasks. Through extensive experiments on HSI super-resolution, compressed sensing, and inpainting, we demonstrate that our approach often achieves superior performance, which is competitive with or even better than the state-of-the-art on each task, via a single model without any task-specific training.
arXiv commentcode at https://github.com/Zeqiang-Lai/DPHSIR
References in corpus (8)
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
- Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network
- Rank Minimization for Snapshot Compressive Imaging
- Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations
- Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
- Hyperspectral Image Classification Based on Sparse Modeling of Spectral Blocks
- TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems