论文标题
图像融合的深卷积稀疏编码网络
Deep Convolutional Sparse Coding Networks for Image Fusion
论文作者
论文摘要
图像融合是许多领域的重要问题，包括数字摄影，计算成像和遥感，仅举几例。最近，深度学习已成为图像融合的重要工具。本文介绍了三种图像融合任务（即红外和可见图像融合，多曝光图像融合和多模式图像融合）的三种深卷积稀疏编码（CSC）网络。 CSC模型以及迭代收缩和阈值算法被推广到字典卷积单元中。结果，从数据中学到了所有超参数。我们的广泛实验和全面比较揭示了拟议网络在定量评估和视觉检查方面的优越性。
Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusion. This paper presents three deep convolutional sparse coding (CSC) networks for three kinds of image fusion tasks (i.e., infrared and visible image fusion, multi-exposure image fusion, and multi-modal image fusion). The CSC model and the iterative shrinkage and thresholding algorithm are generalized into dictionary convolution units. As a result, all hyper-parameters are learned from data. Our extensive experiments and comprehensive comparisons reveal the superiority of the proposed networks with regard to quantitative evaluation and visual inspection.
