DM$^3$Net: Dual-Camera Super-Resolution via Domain Modulation and Multi-scale Matching

• URL: http://arxiv.org/abs/2506.06993v1
• Date: Sun, 08 Jun 2025 04:51:18 GMT
• Title: DM$^3$Net: Dual-Camera Super-Resolution via Domain Modulation and Multi-scale Matching
• Authors: Cong Guan, Jiacheng Ying, Yuya Ieiri, Osamu Yoshie,
• Abstract summary: DM$3$Net is a novel dual-camera super-resolution network based on Domain Modulation and Multi-scale Matching.<n>We introduce Key Pruning to achieve a significant reduction in memory usage and inference time with little model performance sacrificed.
• Score: 4.5275109094772485
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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