Hyperspectral image restoration via the collaboration of low-rank tensor denoising and completion

Abstract

Hyperspectral images (HSIs) are always damaged by various types of noise during acquisition and transmission. Low-rank tensor denoising methods have achieved state-of-the-art results in current HSIs restoration tasks. However, all these methods remove the mixed noise in HSI based on the representation of image prior information. In this paper, we consider a problem for the first time: Structured noise like stripes and deadlines confounds image priors, hindering effective image-noise separation in current approaches. Motivated by this, a new HSI restoration model based on the collaboration of low-rank tensor denoising and completion (LR-TDTC) is proposed. Firstly, the structured noise detection algorithm is applied to identify the positions of structured noise such as stripes and deadlines, achieving the separation of unstructured noise and structured noise. The entries in the structured noisy area are removed. Then, for unstructured noise, a tensor denoising module (TD) based on image prior representation is introduced to separate images and noise. For structured noise, a tensor completion module (TC) based on full-mode-augmentation tensor train rank minimization is introduced to complete the noise area. Finally, the two modules collaborate through the mutual utilization of information to achieve the restoration of the entire image. To solve the LR-TDTC model, a variable tessellation iterative algorithm (VTI) is proposed. VTI utilizes a serialization strategy to enable TD and TC modules to effectively utilize each other's latest iteration results, achieving efficient collaboration between the two. The mixed noise removal experiments on multiple HSIs show that the proposed method has outstanding advantages.