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PEDNet: Physics-guided Encoder-Decoder Network for Image Dehazing
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AI
DOI：10.2352/J.ImagingSci.Technol.2025.69.6.060503.png)
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Abstract
En 中文
We propose a new convolutional neural network called Physics-guided Encoder-Decoder Network (PEDNet) designed for end-to-end single image dehazing. The network uses a reformulated atmospheric scattering model, which is embedded into the network for end-to-end learning. The overall structure is in the form of an encoder-decoder, which fully extracts and fuses contextual information from four different scales through skip connections. In addition, in view of the uneven spread of haze in the real world, we design a Res2FA module based on Res2Net, which introduces a Feature Attention block that is able to focus on important information at a finer granularity. The PEDNet is more adaptable when handling various hazy image types since it employs a physically driven dehazing model. The efficacy of every network module is demonstrated by ablation experiment results. Our suggested solution is superior to current state-of-the-art methods according to experimental results from both synthetic and real-world datasets.
Keywords:
dehazing
deep-learning-based
encoder-decoder net-work
atmospheric scattering model
Journal
J
IF:
0.5
Papers:
62
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0