  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.
