For hyperspectral image(HSI) restoration, how to effectively embed prior information in the model and correctly model the noise have always been the two focus of research. As a domain-dependent prior knowledge, side information has succeeded in many aspects, but it has not received much attention in the field of hyperspectral denoising. In order to naturally couple this domain knowledge with the hyperspectral restoration model, the method linked side information to the underlying matrix representing the potential low-rank structure of the observation data via a bilinear mapping, and used E-3DTV to encode HSI local smoothness prior. In addition, this method used the L_p norm for noise modeling to further enhance the robustness against corruption. This method was compared with five competitive methods on three numerical indicators in two data sets and seven noise addition methods. The results fully reflect the effectiveness and universality of the proposed method for complex noise scene.

[2021-10-05] Printed Article /doc/2020.12.0564/v2