Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion
This work addresses image fusion problems for signal and image processing applications, but it appears incremental as it extends existing SSA models to convolutional settings.
The paper tackled the problem of representing dependent signals using sparse vectors with identical supports by proposing convolutional simultaneous sparse approximation (CSSA) algorithms based on the alternating direction method of multipliers, and applied them to multimodal and multifocus image fusion, though no concrete numerical results were provided.
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.