Hyperspectral image denoising using constraint smooth rank approximation and weighted enhance 3DTV

Abstract

The removal of mixed noise in hyperspectral images (HSI) plays a vital role in subsequent applications. Recently, the smooth rank approximation (SRA) method based on low-rank regularization shows strong property. However, it is difficult for a single regularization to describe prior information of HSI accurately and fully utilized spatial-spectral information, which makes SRA method cannot recover image details completely and also not good enough for the removal of strong mixed noise. This paper proposes a constrained SRA combined with weighted enhanced 3D total variation regularization (SAWTV) HSI restoration frameworks. In addition, considering the intrinsic group relations of different band to spatial and spectral difference images, a constrained SRA combined with weighted group sparsity regularization (SAWGS) restoration frameworks also be proposed. Among them, the introduction of rank constraint into the SRA model can better describe the low-rank characteristics of HSI and improve the denoising effect. Combined with weighted E3DTV regularization which are based on l1-norm and l2,1-norm respectively, it enhances the model's ability to restore local details and ensures the spatial-spectral smoothness. The ADMM method is used to solve the model. A large number of experiments on simulated and real HSIs show that this method has better performance in removing mixed noise than the latest method based on low rank and TV.