ZRDNet: zero-reference image defogging by physics-based decomposition-reconstruction mechanism and perception fusion

This paper investigates challenging fully unsupervised defogging problems, i.e., how to remove fog by feeding only foggy images in deep neural networks rather than using paired or unpaired synthetic images, and how to overcome the problems of insufficient structure and detail recovery in existing unsupervised defogging methods. For this purpose, a zero-reference image defogging method (ZRDNet) is proposed to solve these two problems. Specifically, we develop an unsupervised defogging network consisting of a layer decomposition network and a perceptual fusion network, which are separately optimized by joint multiple-loss based on the stage-wise learning. The decomposition network guides the image decomposition-reconstruction process by rationally constructing loss functions. The fusion network further enhances the details and contrast of the defogged images by fusing the decomposition-reconstruction results. The joint multiple-loss optimization strategy based on the stage-wise learning guides decomposition and fusion tasks, which are completed stage-by-stage. Additionally, a non-reference loss is constructed to prevent artifacts and distortion induced by transmission value deviation. Our method is completely unsupervised, and training only relies on fog images and information derived from the fog images themselves. Experiments are conducted to demonstrate that our ZRDNet, which overcomes the problems of insufficient structure and detail recovery, and domain shift induced by using synthetic image, achieves favorable performance.