  Image restoration is a challenging and ill-posed problem which also has been
a long-standing issue. However, most of learning based restoration methods are
proposed to target one degradation type which means they are lack of
generalization. In this paper, we proposed a multi-branch restoration model
inspired from the Human Visual System (i.e., Retinal Ganglion Cells) which can
achieve multiple restoration tasks in a general framework. The experiments show
that the proposed multi-branch architecture, called CMFNet, has competitive
performance results on four datasets, including image dehazing, deraindrop, and
deblurring, which are very common applications for autonomous cars. The source
code and pretrained models of three restoration tasks are available at
https://github.com/FanChiMao/CMFNet.
