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A multi-expose fusion image dehazing based on scene depth information

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Abstract

Haze weather can lead to reduced visibility of captured images, affecting daily production and life. In this paper, a new defogging technique for multi-exposure images combined with prior algorithm is proposed. Firstly, the transmittance of different regions of the haze image is calculated to obtain more accurate prior information. Secondly, gamma correction is applied to the prior map to obtain a set of multiple underexposure images. Thirdly, for the difference between global features and local details in image defogging, the multiple underexposure image set is decomposed into base and detail layers using guided filtering, and the fusion weight maps of the base layers image patches and the detail layers Laplacian decomposition are constructed, respectively. Finally, the haze-free image is reconstructed and restored. The haze image is selected from a standard dataset with different haze concentrations and compared with the commonly used haze removal algorithms. The defogging effect of this algorithm has better performance in visual effect and objective evaluation index.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Hai Yao & Huawang Qin

  2. School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, 230041, China

    Qian Wu

  3. School of Information Science and Technology, Northwest University, Xi’an, 710127, China

    Zhisong Bi & Xuezhu Wang

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  1. Hai Yao
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  2. Huawang Qin
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Correspondence to Huawang Qin.

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Yao, H., Qin, H., Wu, Q. et al. A multi-expose fusion image dehazing based on scene depth information. Vis Comput 39, 4855–4867 (2023). https://doi.org/10.1007/s00371-022-02632-w

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