Deep hybrid model for single image dehazing and detail refinement

Deep learning technologies have been applied in Single Image Dehazing (SID) tasks successfully. However, most SID algorithms seldom consider to refine image details during dehazing. Therefore, there exist some detail-loss regions in dehazed results. To solve this issue, we design a deep hybrid network to improve dehazing performance and remedy the loss of details. Different from existing algorithms that usually ignore detail refinement and adopt a unified framework to remove haze, we propose to treat dehazing and detail refinement as two separate tasks, so that each task could be solved via different ways. Particularly, we design two sub-networks with a multi-term loss function. First, for removing haze effectively, we introduce the Squeeze-and-Excitation (SE) to design a haze residual attention sub-network, which is used to reconstruct the dehazed image. Second, as for remedying details, we take the previous dehazed image as the input to a detail refinement sub-network, where the image details can be enhanced via multi-scale contextual information aggregation. Through the joint training of two sub-network, the haze can be removed clearly and the image details can be preserved well. Moreover, the detail refinement sub-network can be detached into other existing dehazing methods to improve their model performances. Extensive experiments also verify the superiority of our proposed network against recently proposed state-of-the-arts.