Depth Information Assisted Collaborative Mutual Promotion Network for Single Image Dehazing

TL;DR

This paper introduces a dual-task framework that jointly optimizes depth estimation and image dehazing, leveraging their mutual reinforcement to improve the quality of dehazed images from a single hazy input.

Contribution

It proposes a novel collaborative mutual promotion network integrating depth estimation with dehazing, using difference perception to enhance both tasks simultaneously.

Findings

Outperforms state-of-the-art dehazing methods

Improves depth estimation accuracy in hazy conditions

Enhances dehazed image quality through depth guidance

Abstract

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.