Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion

Zixiang Zhao, Jiangshe Zhang, Haowen Bai, Yicheng Wang, Yukun Cui, Lilun Deng, Kai Sun, Chunxia Zhang, Junmin Liu, Shuang Xu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 2369-2377

Abstract

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 CSCFuse, which contains 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-spectral 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 CSCFuse with regard to quantitative evaluation and visual inspection.

Related Material

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[bibtex]
@InProceedings{Zhao_2023_CVPR, author = {Zhao, Zixiang and Zhang, Jiangshe and Bai, Haowen and Wang, Yicheng and Cui, Yukun and Deng, Lilun and Sun, Kai and Zhang, Chunxia and Liu, Junmin and Xu, Shuang}, title = {Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {2369-2377} }