SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing
Abstract Single-image dehazing technology plays a significant role in video surveillance and intelligent transportation. However, existing dehazing methods using vanilla convolution only extract features in the temporal domain and lack the ability to capture multi-directional information. To address...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-92061-1 |
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| Summary: | Abstract Single-image dehazing technology plays a significant role in video surveillance and intelligent transportation. However, existing dehazing methods using vanilla convolution only extract features in the temporal domain and lack the ability to capture multi-directional information. To address the aforementioned issues, we design a new full spectral attention-based detail enhancement dehazing network, named SAD-Net. SAD-Net adopts a U-Net-like structure and integrates Spectral Detail Enhancement Convolution (SDEC) and Frequency-Guided Attention (FGA). SDEC combines wavelet transform and difference convolution(DC) to enhance high-frequency features while preserving low-frequency information. FGA detects haze-induced discrepancies and fine-tunes feature modulation. Experimental results show that SAD-Net outperforms six other dehazing networks on the Dense-Haze, NH-Haze, RESIDE and I-Haze datasets. Specifically, it increases the peak signal-to-noise ratio (PSNR) to 17.16 dB on the Dense-Haze dataset, surpassing the current state-of-the-art (SOTA) methods. Additionally, SAD-Net achieves excellent dehazing performance on an external dataset without any prior training. |
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| ISSN: | 2045-2322 |