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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| Format: | Article |
| Language: | English |
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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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| _version_ | 1849737752127471616 |
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| author | Qingjun Niu Kun Wu Jialu Zhang Zhenqi Han Lizhuang Liu |
| author_facet | Qingjun Niu Kun Wu Jialu Zhang Zhenqi Han Lizhuang Liu |
| author_sort | Qingjun Niu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4d10c3eea3444dc5ad224b1402c25a4d |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4d10c3eea3444dc5ad224b1402c25a4d2025-08-20T03:06:50ZengNature PortfolioScientific Reports2045-23222025-04-0115111310.1038/s41598-025-92061-1SAD-Net: a full spectral self-attention detail enhancement network for single image dehazingQingjun Niu0Kun Wu1Jialu Zhang2Zhenqi Han3Lizhuang Liu4Shanghai Advanced Research Institute, Chinese Academy of SciencesShanghai Advanced Research Institute, Chinese Academy of SciencesShanghai Advanced Research Institute, Chinese Academy of SciencesShanghai Advanced Research Institute, Chinese Academy of SciencesShanghai Advanced Research Institute, Chinese Academy of SciencesAbstract 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.https://doi.org/10.1038/s41598-025-92061-1 |
| spellingShingle | Qingjun Niu Kun Wu Jialu Zhang Zhenqi Han Lizhuang Liu SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing Scientific Reports |
| title | SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing |
| title_full | SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing |
| title_fullStr | SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing |
| title_full_unstemmed | SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing |
| title_short | SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing |
| title_sort | sad net a full spectral self attention detail enhancement network for single image dehazing |
| url | https://doi.org/10.1038/s41598-025-92061-1 |
| work_keys_str_mv | AT qingjunniu sadnetafullspectralselfattentiondetailenhancementnetworkforsingleimagedehazing AT kunwu sadnetafullspectralselfattentiondetailenhancementnetworkforsingleimagedehazing AT jialuzhang sadnetafullspectralselfattentiondetailenhancementnetworkforsingleimagedehazing AT zhenqihan sadnetafullspectralselfattentiondetailenhancementnetworkforsingleimagedehazing AT lizhuangliu sadnetafullspectralselfattentiondetailenhancementnetworkforsingleimagedehazing |