Deep Convolutional Sparse Coding Networks for Image Fusion
This work addresses image fusion problems in fields like digital photography and remote sensing, presenting an incremental improvement through learned hyper-parameters.
The paper tackled image fusion tasks by proposing deep convolutional sparse coding networks, achieving superior results in quantitative evaluation and visual inspection compared to existing methods.
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.