论文标题
卷积同时稀疏近似与RGB-NIR图像融合的应用
Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion
论文作者
论文摘要
同时稀疏近似（SSA）试图使用具有相同支撑的稀疏向量表示一组依赖信号。 SSA模型已用于涉及多个相关输入信号的各种信号和图像处理应用中。在本文中，我们根据乘数的交替方向方法提出了卷积SSA（CSSA）的算法。具体而言，我们基于SSA模型来解决不同稀疏结构的CSSA问题以及多模式数据/信号中的卷积特征学习问题。我们通过将其应用于多模式和多焦点图像融合问题来评估所提出的算法。
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.
