MULTI-SPECTRAL IMAGE DENOISING WITH SHARED DICTIONARIES AND LOW-RANK REPRESENTATION

As a 3-order tensor, a multi-spectral image (MSI) has dozens of spectral bands, which can deliver more faithful representation for real scenes. However, MSIs are often corrupted by noise in the sensing process, which deteriorates the performance of higher-level classification and recognition tasks. In this paper, we propose a novel tensor dictionaries learning method for MSI denoising, where two shared dictionaries are learned from MSI groups of similar blocks in the spatial domain and the spectral domain, respectively. In addition, we enforce a low rank structure for the representations of MSI groups under the learned dictionaries, which captures the latent structure in MSIs. Our experiments demonstrate that the proposed method achieves the best performance in comparison with the state-of-the-art methods.