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

arXiv: https://arxiv.org/abs/2203.09913 · HTML: https://mlsanity.com/paper/2203.09913/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [Scalable Online Convolutional Sparse Coding](https://mlsanity.com/paper/1706.06972/) (2017)
- [Coupled Feature Learning for Multimodal Medical Image Fusion](https://mlsanity.com/paper/2102.08641/) (2021)
- [Efficient ADMM-based Algorithms for Convolutional Sparse Coding](https://mlsanity.com/paper/2109.02969/) (2021)

## Abstract (arXiv)

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.