A Hybrid Registration and Fusion Method for Hyperspectral Super-resolution

TL;DR

This paper introduces a hybrid registration and fusion method for hyperspectral super-resolution that integrates image alignment with fusion, leveraging low-rank and group-sparse structures, and demonstrates improved performance through theoretical analysis and experiments.

Contribution

The proposed RAF-NLRGS model simultaneously performs registration and fusion, incorporating low-rank and group-sparse structures, with proven error bounds and convergence analysis.

Findings

Effective fusion of hyperspectral and multispectral images demonstrated.

Simultaneous registration and fusion improve super-resolution quality.

Theoretical guarantees support the method's reliability.

Abstract

Fusing hyperspectral images (HSIs) with multispectral images (MSIs) has become a mainstream approach to enhance the spatial resolution of HSIs. Many HSI-MSI fusion methods have achieved impressive results. Nevertheless, certain challenges persist, including: (a) A majority of current methods rely on accurate registration of HSI and MSI, which can be challenging in real-world applications.(b) The obtained HSI-MSI pairs may not be fully utilized. In this paper, we propose a hybrid registration and fusion constrained optimization model named RAF-NLRGS. With respect to challenge (a), the RAF model integrates batch image alignment within the fusion process, facilitating simultaneous execution of image registration and fusion. To address issue (b), the NLRGS model incorporates a nonconvex low-rank and group-sparse structure, leveraging group sparsity to effectively harness valuable information embedded in the residual data. Moreover, the NLRGS model can further enhance fusion performance based on the RAF model. Subsequently, the RAF-NLRGS model is solved within the framework of Generalized Gauss-Newton (GGN) algorithm and Proximal Alternating Optimization (PAO) algorithm. Theoretically, we establish the error bounds for the NLRGS model and the convergence analysis of corresponding algorithms is also presented. Finally, extensive numerical experiments on HSI datasets are conducted to verify the effectiveness of our method.