DM$^3$Net: Dual-Camera Super-Resolution via Domain Modulation and Multi-scale Matching
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908398785658880 |
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| author | Guan, Cong Ying, Jiacheng Ieiri, Yuya Yoshie, Osamu |
| author_facet | Guan, Cong Ying, Jiacheng Ieiri, Yuya Yoshie, Osamu |
| contents | Dual-camera super-resolution is highly practical for smartphone photography that primarily super-resolve the wide-angle images using the telephoto image as a reference. In this paper, we propose DM$^3$Net, a novel dual-camera super-resolution network based on Domain Modulation and Multi-scale Matching. To bridge the domain gap between the high-resolution domain and the degraded domain, we learn two compressed global representations from image pairs corresponding to the two domains. To enable reliable transfer of high-frequency structural details from the reference image, we design a multi-scale matching module that conducts patch-level feature matching and retrieval across multiple receptive fields to improve matching accuracy and robustness. Moreover, we also introduce Key Pruning to achieve a significant reduction in memory usage and inference time with little model performance sacrificed. Experimental results on three real-world datasets demonstrate that our DM$^3$Net outperforms the state-of-the-art approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06993 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DM$^3$Net: Dual-Camera Super-Resolution via Domain Modulation and Multi-scale Matching Guan, Cong Ying, Jiacheng Ieiri, Yuya Yoshie, Osamu Computer Vision and Pattern Recognition Dual-camera super-resolution is highly practical for smartphone photography that primarily super-resolve the wide-angle images using the telephoto image as a reference. In this paper, we propose DM$^3$Net, a novel dual-camera super-resolution network based on Domain Modulation and Multi-scale Matching. To bridge the domain gap between the high-resolution domain and the degraded domain, we learn two compressed global representations from image pairs corresponding to the two domains. To enable reliable transfer of high-frequency structural details from the reference image, we design a multi-scale matching module that conducts patch-level feature matching and retrieval across multiple receptive fields to improve matching accuracy and robustness. Moreover, we also introduce Key Pruning to achieve a significant reduction in memory usage and inference time with little model performance sacrificed. Experimental results on three real-world datasets demonstrate that our DM$^3$Net outperforms the state-of-the-art approaches. |
| title | DM$^3$Net: Dual-Camera Super-Resolution via Domain Modulation and Multi-scale Matching |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.06993 |