Single Image Dehazing Using Scene Depth Ordering

TL;DR

This paper introduces a novel single image dehazing method guided by depth order, which preserves scene depth perception and improves restoration quality by utilizing depth cues to guide transmission estimation.

Contribution

It proposes a depth order guided dehazing approach that effectively maintains scene depth consistency and enhances image restoration compared to existing methods.

Findings

Better recovery of scene structure and vivid colors

Higher computational efficiency than state-of-the-art methods

Effective use of depth order as a global constraint

Abstract

Images captured in hazy weather generally suffer from quality degradation, and many dehazing methods have been developed to solve this problem. However, single image dehazing problem is still challenging due to its ill-posed nature. In this paper, we propose a depth order guided single image dehazing method, which utilizes depth order in hazy images to guide the dehazing process to achieve a similar depth perception in corresponding dehazing results. The consistency of depth perception ensures that the regions that look farther or closer in hazy images also appear farther or closer in the corresponding dehazing results, and thus effectively avoid the undesired visual effects. To achieve this goal, a simple yet effective strategy is proposed to extract the depth order in hazy images, which offers a reference for depth perception in hazy weather. Additionally, a depth order embedded transformation model is devised, which performs transmission estimation under the guidance of depth order to realize an unchanged depth order in the dehazing results. The extracted depth order provides a powerful global constraint for the dehazing process, which contributes to the efficient utilization of global information, thereby bringing an overall improvement in restoration quality. Extensive experiments demonstrate that the proposed method can better recover potential structure and vivid color with higher computational efficiency than the state-of-the-art dehazing methods.