GTFMN: Guided Texture and Feature Modulation Network for Low-Light Image Enhancement and Super-Resolution
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914283445551104 |
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| author | Huang, Yongsong Peng, Tzu-Hsuan Miyazaki, Tomo Liu, Xiaofeng Chou, Chun-Ting Pang, Ai-Chun Omachi, Shinichiro |
| author_facet | Huang, Yongsong Peng, Tzu-Hsuan Miyazaki, Tomo Liu, Xiaofeng Chou, Chun-Ting Pang, Ai-Chun Omachi, Shinichiro |
| contents | Low-light image super-resolution (LLSR) is a challenging task due to the coupled degradation of low resolution and poor illumination. To address this, we propose the Guided Texture and Feature Modulation Network (GTFMN), a novel framework that decouples the LLSR task into two sub-problems: illumination estimation and texture restoration. First, our network employs a dedicated Illumination Stream whose purpose is to predict a spatially varying illumination map that accurately captures lighting distribution. Further, this map is utilized as an explicit guide within our novel Illumination Guided Modulation Block (IGM Block) to dynamically modulate features in the Texture Stream. This mechanism achieves spatially adaptive restoration, enabling the network to intensify enhancement in poorly lit regions while preserving details in well-exposed areas. Extensive experiments demonstrate that GTFMN achieves the best performance among competing methods on the OmniNormal5 and OmniNormal15 datasets, outperforming them in both quantitative metrics and visual quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19157 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GTFMN: Guided Texture and Feature Modulation Network for Low-Light Image Enhancement and Super-Resolution Huang, Yongsong Peng, Tzu-Hsuan Miyazaki, Tomo Liu, Xiaofeng Chou, Chun-Ting Pang, Ai-Chun Omachi, Shinichiro Computer Vision and Pattern Recognition Low-light image super-resolution (LLSR) is a challenging task due to the coupled degradation of low resolution and poor illumination. To address this, we propose the Guided Texture and Feature Modulation Network (GTFMN), a novel framework that decouples the LLSR task into two sub-problems: illumination estimation and texture restoration. First, our network employs a dedicated Illumination Stream whose purpose is to predict a spatially varying illumination map that accurately captures lighting distribution. Further, this map is utilized as an explicit guide within our novel Illumination Guided Modulation Block (IGM Block) to dynamically modulate features in the Texture Stream. This mechanism achieves spatially adaptive restoration, enabling the network to intensify enhancement in poorly lit regions while preserving details in well-exposed areas. Extensive experiments demonstrate that GTFMN achieves the best performance among competing methods on the OmniNormal5 and OmniNormal15 datasets, outperforming them in both quantitative metrics and visual quality. |
| title | GTFMN: Guided Texture and Feature Modulation Network for Low-Light Image Enhancement and Super-Resolution |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.19157 |