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.

翻译：从单张有雾图像中恢复清晰图像是一个开放的逆问题。尽管已有显著的研究进展，但大多数现有方法忽略了下游任务对促进上游去雾的影响。从雾霾生成机制的角度来看，场景深度信息与有雾图像之间存在潜在关系。基于此，我们提出了一种双任务协同互促框架，以实现单张图像的去雾。该框架通过双任务交互机制融合深度估计与去雾，并实现两者性能的相互增强。为了完成这两项任务的联合优化，我们开发了一种具有差异感知的交替实现机制。一方面，通过提出去雾结果与理想图像之间深度图的差异感知，促使去雾网络关注去雾中的非理想区域；另一方面，通过提升有雾图像中难以恢复区域的深度估计性能，使去雾网络能够显式利用有雾图像的深度信息辅助清晰图像恢复。为了促进深度估计，我们提出利用去雾图像与真实图像之间的差异引导深度估计网络聚焦于去雾后的非理想区域。这使得去雾与深度估计能够在相互强化中发挥各自优势。实验结果表明，所提方法能够取得优于现有最新方法的性能。