Image defogging based on Multi-input and Multi-scale UNet

The coarse-to-fine image defogging strategy has been widely used in the structural design of individual image defogging networks. In the traditional method, multi-scale input image subnets are superimposed, so that the sharpness of the image is gradually improved from the bottom subnet to the top subnet, which inevitably leads to the loss of image details. Toward a fast and accurate dehazing network design, we revisit the coarse-to-fine strategy and present a multi-input and multi-scale U-Net (MIMS-UNet). The MIMS-UNet has two distinct features. On the one hand, the single-encoder of MIMS-UNet adopts multi-input and multi-scale image, which increases the computation amount but greatly improves the network performance. On the other hand, codec structures with context blocks are used to capture context information and recover more details. The experimental results show that the proposed method achieves good results in both quantification and visualization. Compared with the existing methods, the proposed network can achieve ideal results of defogging and effectively avoid color distortion after defogging.