Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910420788314112 |
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| author | Chen, Genggeng Dai, Kexin Yang, Kangzhen Hu, Tao Chen, Xiangyu Yang, Yongqing Dong, Wei Wu, Peng Zhang, Yanning Yan, Qingsen |
| author_facet | Chen, Genggeng Dai, Kexin Yang, Kangzhen Hu, Tao Chen, Xiangyu Yang, Yongqing Dong, Wei Wu, Peng Zhang, Yanning Yan, Qingsen |
| contents | In real-world scenarios, due to a series of image degradations, obtaining high-quality, clear content photos is challenging. While significant progress has been made in synthesizing high-quality images, previous methods for image restoration and enhancement often overlooked the characteristics of different degradations. They applied the same structure to address various types of degradation, resulting in less-than-ideal restoration outcomes. Inspired by the notion that high/low frequency information is applicable to different degradations, we introduce HLNet, a Bracketing Image Restoration and Enhancement method based on high-low frequency decomposition. Specifically, we employ two modules for feature extraction: shared weight modules and non-shared weight modules. In the shared weight modules, we use SCConv to extract common features from different degradations. In the non-shared weight modules, we introduce the High-Low Frequency Decomposition Block (HLFDB), which employs different methods to handle high-low frequency information, enabling the model to address different degradations more effectively. Compared to other networks, our method takes into account the characteristics of different degradations, thus achieving higher-quality image restoration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13537 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition Chen, Genggeng Dai, Kexin Yang, Kangzhen Hu, Tao Chen, Xiangyu Yang, Yongqing Dong, Wei Wu, Peng Zhang, Yanning Yan, Qingsen Image and Video Processing Computer Vision and Pattern Recognition In real-world scenarios, due to a series of image degradations, obtaining high-quality, clear content photos is challenging. While significant progress has been made in synthesizing high-quality images, previous methods for image restoration and enhancement often overlooked the characteristics of different degradations. They applied the same structure to address various types of degradation, resulting in less-than-ideal restoration outcomes. Inspired by the notion that high/low frequency information is applicable to different degradations, we introduce HLNet, a Bracketing Image Restoration and Enhancement method based on high-low frequency decomposition. Specifically, we employ two modules for feature extraction: shared weight modules and non-shared weight modules. In the shared weight modules, we use SCConv to extract common features from different degradations. In the non-shared weight modules, we introduce the High-Low Frequency Decomposition Block (HLFDB), which employs different methods to handle high-low frequency information, enabling the model to address different degradations more effectively. Compared to other networks, our method takes into account the characteristics of different degradations, thus achieving higher-quality image restoration. |
| title | Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.13537 |