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FDDN: frequency-guided network for single image dehazing

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Abstract

Haze appearing in natural scene images generally contains nonhomogeneous characteristics such as filaments, masses, and mist. The high-frequency part of hazy images contains variable background textures and haze shapes, whereas regions with mostly uniform distribution are dominated by low-frequency information. Although existing methods based on convolutional neural networks have achieved remarkable progress in single image dehazing, the intrinsic hazy image patterns have been neglected in most models. We propose a frequency division dehazing network to leverage prior knowledge characterizing hazy images. The proposed network processes shallow feature maps through high-, medium-, and low-frequency branches. This separation facilitates a flexible architecture, whose branch handling lower-frequency components is less redundant given its relatively simpler background and haze shapes. Then, by integrating knowledge extracted from all the network branches using feature fusion, the proposed network fully exploits the variety of frequency characteristics in hazy images and achieves 39.51 PSNR and 0.9931 SSIM on the RESIDE dataset. Experiments on both synthetic and real hazy images demonstrate the superiority of the proposed network over several existing state-of-the-art methods, demonstrating the effectiveness of exploiting prior knowledge in hazy images.

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Data availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work is supported by the Basic Public Welfare Research Program of Zhejiang Province (LGG22F020036), National Natural Science Foundation of China (12001005), and Natural Science Foundation of Anhui Province (2008085QF286).

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Authors and Affiliations

  1. School of Information Engineering, Zhejiang Ocean University, Zhoushan, 316000, People’s Republic of China

    Haozhen Shen & Chao Wang

  2. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhoushan, 316000, People’s Republic of China

    Chao Wang

  3. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Liangjian Deng

  4. School of Mathematical Sciences, Anhui University, Hefei, 230601, People’s Republic of China

    Liangtian He

  5. Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macao, People’s Republic of China

    Xiaoping Lu

  6. School of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, People’s Republic of China

    Mingwen Shao

  7. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Deyu Meng

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  1. Haozhen Shen
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  2. Chao Wang
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  3. Liangjian Deng
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Correspondence to Chao Wang.

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Shen, H., Wang, C., Deng, L. et al. FDDN: frequency-guided network for single image dehazing. Neural Comput & Applic 35, 18309–18324 (2023). https://doi.org/10.1007/s00521-023-08637-3

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