Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising

TL;DR

This paper introduces Graph-SSTV, a novel regularization method for hyperspectral image denoising that leverages graph-based spatial structure to better preserve edges and textures under high noise conditions.

Contribution

The paper proposes a new graph-based total variation regularization called Graph-SSTV, improving noise removal while preserving spatial details in hyperspectral images.

Findings

GSSTV outperforms existing models in mixed noise removal

The graph-based approach effectively preserves edges and textures

The algorithm is efficient and suitable for practical applications

Abstract

The spatio-spectral total variation (SSTV) model has been widely used as an effective regularization of hyperspectral images (HSI) for various applications such as mixed noise removal. However, since SSTV computes local spatial differences uniformly, it is difficult to remove noise while preserving complex spatial structures with fine edges and textures, especially in situations of high noise intensity. To solve this problem, we propose a new TV-type regularization called Graph-SSTV (GSSTV), which generates a graph explicitly reflecting the spatial structure of the target HSI from noisy HSIs and incorporates a weighted spatial difference operator designed based on this graph. Furthermore, we formulate the mixed noise removal problem as a convex optimization problem involving GSSTV and develop an efficient algorithm based on the primal-dual splitting method to solve this problem. Finally, we demonstrate the effectiveness of GSSTV compared with existing HSI regularization models through experiments on mixed noise removal. The source code will be available at https://www.mdi.c.titech.ac.jp/publications/gsstv.