Images taken under haze weather suffer from low contrast and color shift,which can further negatively affect the accuracy of the following high-level computer vision applications. As one popular technology in image dehazing,the atmospheric scattering model ( ASM) is gaining attention from researchers. However,the existing ASM-based methods suffer from the dim effect because they ignore the light-trapping phenomenon. To address this problem,a light absorption-based interpretable network for image dehazing is proposed. Specifically,a light absorption-based ASM( LA-ASM) is first constructed,in which a light absorption coefficient defined by the texture density and the scene depth is introduced. Moreover,an interpretable network,dubbed LA-ASMNet,in which haze patterns of LA-ASM are learned to assist image dehazing,is built. Simulation results based on RESIDE dataset demonstrate the effectiveness of LA-ASMNet in alleviating the dim effect for image dehazing.
维普
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