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

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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.

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Authors and Affiliations

  1. College of Artificial Intelligence, Southwest University, Chongqing, 400715, China

    Yun Liu & Pengfei Jia

  2. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Hao Zhou

  3. College of Big Data and Computer Science, Guizhou Normal University, Guiyang, 550001, China

    Anzhi Wang

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  1. Yun Liu
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  2. Pengfei Jia
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  3. Hao Zhou
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  4. Anzhi Wang
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Correspondence to Yun Liu.

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This work was supported by Chongqing Natural Science Foundation (Grant no. cstc2020jcyj-msxmX0324), the Fundamental Research Funds for the Central Universities under Project SWU119044, the Construction of Chengdu-Chongqing Economic Circle Science and Technology Innovation Project (Grant no. KJCX2020007 ), the Fundamental Science and Advanced Technology Research Foundation of Chongqing (cstc2018jcyjA0867), the Fundamental Science on Nuclear Wastes and Environmental safety Laboratory (Grant No. 19kfhk03) and Open Research Fund Program of Data Recovery Key Laboratory of Sichuan Province (Grant No. DRN19015).

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Liu, Y., Jia, P., Zhou, H. et al. Joint dehazing and denoising for single nighttime image via multi-scale decomposition. Multimed Tools Appl 81, 23941–23962 (2022). https://doi.org/10.1007/s11042-022-12681-x

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