Depth Information Assisted Collaborative Mutual Promotion Network for
  Single Image Dehazing
Yafei Zhang Shen Zhou Huafeng Li
Recovering a clear image from a single hazy image is an open inverse problem.
Although significant research progress has been made, most existing methods
ignore the effect that downstream tasks play in promoting upstream dehazing.
From the perspective of the haze generation mechanism, there is a potential
relationship between the depth information of the scene and the hazy image.
Based on this, we propose a dual-task collaborative mutual promotion framework
to achieve the dehazing of a single image. This framework integrates depth
estimation and dehazing by a dual-task interaction mechanism and achieves
mutual enhancement of their performance. To realize the joint optimization of
the two tasks, an alternative implementation mechanism with the difference
perception is developed. On the one hand, the difference perception between the
depth maps of the dehazing result and the ideal image is proposed to promote
the dehazing network to pay attention to the non-ideal areas of the dehazing.
On the other hand, by improving the depth estimation performance in the
difficult-to-recover areas of the hazy image, the dehazing network can
explicitly use the depth information of the hazy image to assist the clear
image recovery. To promote the depth estimation, we propose to use the
difference between the dehazed image and the ground truth to guide the depth
estimation network to focus on the dehazed unideal areas. It allows dehazing
and depth estimation to leverage their strengths in a mutually reinforcing
manner. Experimental results show that the proposed method can achieve better
performance than that of the state-of-the-art approaches.