Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion

TL;DR

This paper introduces convolutional simultaneous sparse approximation algorithms for multimodal image fusion, leveraging the SSA model to improve the representation of correlated signals with applications in RGB-NIR image fusion.

Contribution

The paper proposes novel convolutional SSA algorithms based on the alternating direction method of multipliers, addressing different sparsity structures and convolutional feature learning in multimodal data.

Findings

Effective in multimodal image fusion tasks

Improves sparse representation of correlated signals

Demonstrates advantages over existing methods

Abstract

Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports. The SSA model has been used in various signal and image processing applications involving multiple correlated input signals. In this paper, we propose algorithms for convolutional SSA (CSSA) based on the alternating direction method of multipliers. Specifically, we address the CSSA problem with different sparsity structures and the convolutional feature learning problem in multimodal data/signals based on the SSA model. We evaluate the proposed algorithms by applying them to multimodal and multifocus image fusion problems.