Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization

TL;DR

This paper introduces a novel hyperspectral image denoising method that employs subspace representation and weighted low-rank tensor regularization to effectively remove mixed noise while preserving intrinsic image structures.

Contribution

The paper proposes a new hyperspectral denoising approach combining subspace projection and weighted low-rank tensor regularization, improving restoration quality and computational efficiency.

Findings

Outperforms existing methods in quantitative metrics

Preserves spectral and spatial structures effectively

Reduces computational complexity compared to SVD-based methods

Abstract

Recently, the low-rank property of different components extracted from the image has been considered in man hyperspectral image denoising methods. However, these methods usually unfold the 3D tensor to 2D matrix or 1D vector to exploit the prior information, such as nonlocal spatial self-similarity (NSS) and global spectral correlation (GSC), which break the intrinsic structure correlation of hyperspectral image (HSI) and thus lead to poor restoration quality. In addition, most of them suffer from heavy computational burden issues due to the involvement of singular value decomposition operation on matrix and tensor in the original high-dimensionality space of HSI. We employ subspace representation and the weighted low-rank tensor regularization (SWLRTR) into the model to remove the mixed noise in the hyperspectral image. Specifically, to employ the GSC among spectral bands, the noisy HSI is projected into a low-dimensional subspace which simplified calculation. After that, a weighted low-rank tensor regularization term is introduced to characterize the priors in the reduced image subspace. Moreover, we design an algorithm based on alternating minimization to solve the nonconvex problem. Experiments on simulated and real datasets demonstrate that the SWLRTR method performs better than other hyperspectral denoising methods quantitatively and visually.