Low-rankness and Smoothness Meet Subspace: A Unified Tensor Regularization for Hyperspectral Image Super-resolution

TL;DR

This paper introduces JLRST, a unified tensor regularizer for hyperspectral image super-resolution that combines low-rankness and smoothness priors within a subspace framework, improving accuracy and efficiency.

Contribution

It proposes a novel regularization method that jointly encodes priors under a subspace framework, addressing high spectral dimensionality challenges in HSI-SR.

Findings

JLRST outperforms state-of-the-art methods in HSI-SR accuracy.

The mode-3 logarithmic TNN reduces bias in tensor nuclear norm processing.

The method improves computational efficiency by focusing on subspace coefficients.

Abstract

Hyperspectral image super-resolution (HSI-SR) has emerged as a challenging yet critical problem in remote sensing. Existing approaches primarily focus on regularization techniques that leverage low-rankness and local smoothness priors. Recently, correlated total variation has been introduced for tensor recovery, integrating these priors into a single regularization framework. Direct application to HSI-SR, however, is hindered by the high spectral dimensionality of hyperspectral data. In this paper, we propose a unified tensor regularizer, called JLRST, which jointly encodes low-rankness and local smoothness priors under a subspace framework. Specifically, we compute the gradients of the clustered coefficient tensors along all three tensor modes to fully exploit spectral correlations and nonlocal similarities in HSI. By enforcing priors on subspace coefficients rather than the entire HR-HSI data, the proposed method achieves improved computational efficiency and accuracy. Furthermore, to mitigate the bias introduced by the tensor nuclear norm (TNN), we introduce the mode-3 logarithmic TNN to process gradient tensors. An alternating direction method of multipliers with proven convergence is developed to solve the proposed model. Experimental results demonstrate that our approach significantly outperforms state-of-the-art model-based methods in HSI-SR.