Joint dehazing and denoising for single nighttime image via multi-scale decomposition

Yun Liu, Pengfei Jia, Hao Zhou, Anzhi Wang

Abstract: Outdoor images taken in the foggy or haze weather conditions are usually contaminated due to the presence of turbid medium in the atmosphere. Moreover, images captured under nighttime haze scenarios will be degraded even further owing to some unexpected factors. However, most existing dehazing methods mainly focus on daytime haze scenes, which cannot effectively remove the haze and suppress the noise for nighttime hazy images. To overcome these intractable problems, a joint dehazing and denoising framework for nighttime haze scenes is proposed based on multi-scale decomposition. First, the glow is removed by using its characteristic of the relative smoothness and the gamma correction operation is employed on the glow-free image for improving the overall brightness. Then, we adopt the multi-scale strategy to decompose the nighttime hazy image into a structure layer and multiple texture layers based on the total variation. Subsequently, the structure layer is dehazed based on the dark channel prior (DCP) and the texture layers are denoised based on color block-matching 3D filtering (CBM3D) prior to enhancement. Finally, the dehazed structure layer and the enhanced texture layers are fused into a dehazing result. Experiments on real-world and synthetic nighttime hazy images reveal that the proposed nighttime dehazing framework outperforms other state-of-the-art daytime and nighttime dehazing techniques.