Adaptive tensor matrix based kernel regression for hyperspectral image denoising

Abstract

Kernel regression has been shown to be a powerful image denoising technique. In this paper, a three-dimensional (3-D) kernel regression hyperspectral image (HSI) denoising mechanism is proposed. The main contributions of this paper can be summarized as follows: Three orientation vectors and the corresponding coefficients are presented, which are adaptive for each pixel based on the innovation of 2-D structure tensor. An adaptive-driven 3-D tensor matrix is proposed for kernel regression, in which the spatial geometric structure and spectrum continuity are both considered. The proposed adaptive kernel regression is applied to HSI denoising. Both stimulated and real data experiments indicate that the proposed method can work well in detail preservation and noise removal.