Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration

TL;DR

This paper introduces a unified iterative framework for hyperspectral image restoration that leverages global spectral low-rank subspace assumptions, improving performance and efficiency across denoising, reconstruction, and inpainting tasks.

Contribution

It proposes a novel paradigm combining spatial and spectral properties, with an efficient alternating minimization algorithm for HSI restoration.

Findings

Superior performance on denoising, reconstruction, and inpainting tasks.

Reduced computational complexity compared to existing methods.

Effective handling of real and simulated hyperspectral datasets.

Abstract

Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) restoration, which includes the tasks of denoising, compressed HSI reconstruction and inpainting. Unfortunately, while its restoration performance benefits from more spectral bands, its runtime also substantially increases. In this paper, we claim that the HSI lies in a global spectral low-rank subspace, and the spectral subspaces of each full band patch group should lie in this global low-rank subspace. This motivates us to propose a unified paradigm combining the spatial and spectral properties for HSI restoration. The proposed paradigm enjoys performance superiority from the non-local spatial denoising and light computation complexity from the low-rank orthogonal basis exploration. An efficient alternating minimization algorithm with rank adaptation is developed. It is done by first solving a fidelity term-related problem for the update of a latent input image, and then learning a low-dimensional orthogonal basis and the related reduced image from the latent input image. Subsequently, non-local low-rank denoising is developed to refine the reduced image and orthogonal basis iteratively. Finally, the experiments on HSI denoising, compressed reconstruction, and inpainting tasks, with both simulated and real datasets, demonstrate its superiority with respect to state-of-the-art HSI restoration methods.