Hyperspectral and Multispectral Image Fusion based on a Sparse Representation

TL;DR

This paper introduces a variational approach for hyperspectral and multispectral image fusion using sparse regularization and learned dictionaries, effectively solving the inverse problem in a lower-dimensional subspace.

Contribution

It proposes a novel fusion method based on sparse regularization with learned dictionaries, improving over existing techniques.

Findings

Demonstrates superior fusion quality compared to state-of-the-art methods.

Efficiently recovers target images in a lower-dimensional subspace.

Uses alternating optimization with ADMM for effective solution.

Abstract

This paper presents a variational based approach to fusing hyperspectral and multispectral images. The fusion process is formulated as an inverse problem whose solution is the target image assumed to live in a much lower dimensional subspace. A sparse regularization term is carefully designed, relying on a decomposition of the scene on a set of dictionaries. The dictionary atoms and the corresponding supports of active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved via alternating optimization with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the state-of-the-art fusion methods.