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论文类型：会议论文

发表时间：2018-01-01

收录刊物：CPCI-S

卷号：2018-April

页面范围：1373-1377

关键字：Robust haze removal; transmission map estimation; deep residual learning; image propagation

摘要：Haze is one of the most important factors which reduce the outdoor image quality. Existing approaches often aim to design their models based on principles of hazes. However, even with exactly modeled haze distribution, it is still a challenging task due to factors in real scenario, such as noises, halos and artifacts. To address limitations of existing approaches for real-world hazy removal problem, this paper proposes a novel framework to incorporate deep residual architectures into a propagation scheme to jointly estimate transmission and clean scene. We evaluate the proposed framework on both widely used benchmarks and real-world low-quality hazy images Extensive experimental results demonstrate that our method performs favorably against approaches designed only based on haze cues and achieves the state-of-the-art results, compared with both conventional shallow models and deep dehzaing networks.