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An end-to-end dehazing network with transitional convolution layer

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Abstract

Image dehazing is a challenging task of restoring a clear image from a haze-polluted image. However, most popular dehazing methods have color shift and overexposure problems owing to the inaccurate estimation of the transmission and atmospheric light in the atmospheric scattering model. In view of the existing problems, this paper proposes a simple but effective dehazing network by learning the residual image between the hazy image and haze-free image. A novel transitional convolution structure is constructed in this network, which contributes to utilizing shallow structure information to enhance the final residual image. Furthermore, to provide images with more realistic color information after dehazing, a novel color difference loss based on CIEDE2000 is designed as one term of the total loss function. In addition, for some real-world images that affect practical applications, a brightness enhancement module is also introduced to restore the luminance of the images. Experiments on synthetic datasets and real-world images demonstrate that the proposed method has clear advantages in both subjective and objective evaluation indicators compared to several existing advanced algorithms.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61862030 and 61662026), by the Natural Science Foundation of Jiangxi Province (Nos. 20182BCB22006, 20181BAB202010, 20192ACB20002, and 20192ACBL21008), and by the Project of the Education Department of Jiangxi Province (Nos. GJJ170312 and GJJ170318).

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Authors and Affiliations

  1. School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Shuying Huang, Hongxia Li & Bin Wang

  2. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Yong Yang

  3. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Shuying Huang & Nini Rao

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  1. Shuying Huang
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Correspondence to Yong Yang.

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Huang, S., Li, H., Yang, Y. et al. An end-to-end dehazing network with transitional convolution layer. Multidim Syst Sign Process 31, 1603–1623 (2020). https://doi.org/10.1007/s11045-020-00723-2

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