Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction

• URL: http://arxiv.org/abs/2203.15325v1
• Date: Tue, 29 Mar 2022 08:11:04 GMT
• Title: Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction
• Authors: De Cheng, Yan Li, Dingwen Zhang, Nannan Wang, Xinbo Gao, Jiande Sun
• Abstract summary: We propose a novel density-variational learning framework to improve the robustness of the image dehzing model. Specifically, the dehazing network is optimized under the consistency-regularized framework. Our method significantly surpasses the state-of-the-art approaches.
• Score: 95.5735805072852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single image dehazing as a fundamental low-level vision task, is essential for the development of robust intelligent surveillance system. In this paper, we make an early effort to consider dehazing robustness under variational haze density, which is a realistic while under-studied problem in the research filed of singe image dehazing. To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios. Specifically, the dehazing network is optimized under the consistency-regularized framework with the proposed Contrast-Assisted Reconstruction Loss (CARL). The CARL can fully exploit the negative information to facilitate the traditional positive-orient dehazing objective function, by squeezing the dehazed image to its clean target from different directions. Meanwhile, the consistency regularization keeps consistent outputs given multi-level hazy images, thus improving the model robustness. Extensive experimental results on two synthetic and three real-world datasets demonstrate that our method significantly surpasses the state-of-the-art approaches.

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