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Indexed by:Symposium

Date of Publication:2018-01-01

Included Journals:CPCI-S

Page Number:2840-2844

Key Words:Jointly dehazing; residual learning; transmission refinement; deep CNN

Abstract:Recently, image dehazing has received extensive attention from researchers in vision society. Previous dehazing methods usually estimate transmissions and haze-free images in a separate way, which leads to poor image dehazing results if transmissions are incorrectly estimated. On the other hand, though some CNN-based deep networks have been developed to remove haze, their transmission estimations heavily rely on white balance. In this paper, we propose a residual type CNN for transmission refinement rather than estimation. Benefit from its residual learning ability, we plug the network in solving an optimization problem, which is able to improve the refinement results through jointly estimating transmissions and clean images in a single framework. Experimental results of synthetic and real-world images demonstrate the superiority and efficiency of our proposed framework, compared to many state-of-the-art methods.