Cluster sparsity field for hyperspectral imagery denoising

Abstract

Hyperspectral images (HSIs) can facilitate extensive computer vision applications with the extra spectra information. However, HSIs often suffer from noise corruption during the practical imaging procedure. Though it has been testified that intrinsic correlation across spectrum and spatial similarity (i.e., local similarity in locally smooth areas and non-local similarity among recurrent patterns) in HSIs are useful for denoising, how to fully exploit them together to obtain a good denoising model is seldom studied. In this study, we present an effective cluster sparsity field based HSIs denoising (CSFHD) method by exploiting those two characteristics simultaneously. Firstly, a novel Markov random field prior, named cluster sparsity field (CSF), is proposed for the sparse representation of an HSI. By grouping pixels into several clusters with spectral similarity, the CSF prior defines both a structured sparsity potential and a graph structure potential on each cluster to model the correlation across spectrum and spatial similarity in the HSI, respectively. Then, the CSF prior learning and the image denoising are unified into a variational framework for optimization, where all unknown variables are learned directly from the noisy observation. This guarantees to learn a data-dependent image model, thus producing satisfying denoising results. Plenty experiments on denoising synthetic and real noisy HSIs validated that the proposed CSFHD outperforms several state-of-the-art methods.