Journal of Dalian Maritime University >

2025 , Vol. 51 >Issue 3: 74 - 84

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2025.03.008

Unsupervised image dehazing network based on CycleGAN

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  • (Marine Electrical Engineering College, Dalian Maritime University, Dalian 116026, China)

Online published: 2025-05-07

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Abstract

The performance of deep learning-based dehazing networks is highly dependent on large and rich paired datasets. However, the collection of paired data consisting of hazy images and clear images in real-world environments is extremely difficult. As a result, existing studies mostly adopted synthetic datasets for training, which leads to insufficient generalization ability of the networks in complex real-world scenarios. Therefore, a CycleGAN-based unsupervised image dehazing network was proposed to solve the problems of color distortion, artifacts, and incomplete dehazing that were prone to occur during the dehazing process of CycleGAN. Firstly, a residual dense connection module was designed to construct the encoder-decoder architecture,while  incorporating spatial and channel residual attention modules.An attention fusion mechanism was introduced in the skip connection section to achieve deep optimization of feature extraction and screening processes.Secondly, a new loss function was designed to effectively balance the visual authenticity and defogging accuracy of generated images.Experimental results show that, compared with baseline methods, the peak signal-to-noise ratio (PSNR) of the dehazed images of the designed network on outdoor synthetic datasets is improved by 22.71%, and the structural similarity (SSIM) value is increased by 6.49%. The image quality is improved  after defogging, and the network can also generate visually realistic foggy images.


Key words： image dehazing; cycle generative adversarial network; loss function; residual dense connection; attention mechanism; synthetic hazy images

Cite this article

LIU Ting, DONG Mengyu, ZHENG Kai, WANG Kun, HUANG Xianyang, LU Xiangyi . Unsupervised image dehazing network based on CycleGAN[J]. Journal of Dalian Maritime University, 2025 , 51(3) : 74 -84 . DOI: 10.16411/j.cnki.issn1006-7736.2025.03.008

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