  In this paper, we propose a novel deep convolutional neural network to solve
the general multi-modal image restoration (MIR) and multi-modal image fusion
(MIF) problems. Different from other methods based on deep learning, our
network architecture is designed by drawing inspirations from a new proposed
multi-modal convolutional sparse coding (MCSC) model. The key feature of the
proposed network is that it can automatically split the common information
shared among different modalities, from the unique information that belongs to
each single modality, and is therefore denoted with CU-Net, i.e., Common and
Unique information splitting network. Specifically, the CU-Net is composed of
three modules, i.e., the unique feature extraction module (UFEM), common
feature preservation module (CFPM), and image reconstruction module (IRM). The
architecture of each module is derived from the corresponding part in the MCSC
model, which consists of several learned convolutional sparse coding (LCSC)
blocks. Extensive numerical results verify the effectiveness of our method on a
variety of MIR and MIF tasks, including RGB guided depth image
super-resolution, flash guided non-flash image denoising, multi-focus and
multi-exposure image fusion.
