MegaSR: Mining Customized Semantics and Expressive Guidance for Real-World Image Super-Resolution
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908685330022400 |
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| author | Li, Xinrui Zhang, Jinrong Wu, Jianlong Chen, Chong Nie, Liqiang Lin, Zhouchen |
| author_facet | Li, Xinrui Zhang, Jinrong Wu, Jianlong Chen, Chong Nie, Liqiang Lin, Zhouchen |
| contents | Text-to-image (T2I) models have ushered in a new era of real-world image super-resolution (Real-ISR) due to their rich internal implicit knowledge for multimodal learning. Although bringing high-level semantic priors and dense pixel guidance have led to advances in reconstruction, we identified several critical phenomena by analyzing the behavior of existing T2I-based Real-ISR methods: (1) Fine detail deficiency, which ultimately leads to incorrect reconstruction in local regions. (2) Block-wise semantic inconsistency, which results in distracted semantic interpretations across U-Net blocks. (3) Edge ambiguity, which causes noticeable structural degradation. Building upon these observations, we first introduce MegaSR, which enhances the T2I-based Real-ISR models with fine-grained customized semantics and expressive guidance to unlock semantically rich and structurally consistent reconstruction. Then, we propose the Customized Semantics Module (CSM) to supplement fine-grained semantics from the image modality and regulate the semantic fusion between multi-level knowledge to realize customization for different U-Net blocks. Besides the semantic adaptation, we identify expressive multimodal signals through pair-wise comparisons and introduce the Multimodal Signal Fusion Module (MSFM) to aggregate them for structurally consistent reconstruction. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of the method. Notably, it not only achieves state-of-the-art performance on quality-driven metrics but also remains competitive on fidelity-focused metrics, striking a balance between perceptual realism and faithful content reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08096 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MegaSR: Mining Customized Semantics and Expressive Guidance for Real-World Image Super-Resolution Li, Xinrui Zhang, Jinrong Wu, Jianlong Chen, Chong Nie, Liqiang Lin, Zhouchen Computer Vision and Pattern Recognition Text-to-image (T2I) models have ushered in a new era of real-world image super-resolution (Real-ISR) due to their rich internal implicit knowledge for multimodal learning. Although bringing high-level semantic priors and dense pixel guidance have led to advances in reconstruction, we identified several critical phenomena by analyzing the behavior of existing T2I-based Real-ISR methods: (1) Fine detail deficiency, which ultimately leads to incorrect reconstruction in local regions. (2) Block-wise semantic inconsistency, which results in distracted semantic interpretations across U-Net blocks. (3) Edge ambiguity, which causes noticeable structural degradation. Building upon these observations, we first introduce MegaSR, which enhances the T2I-based Real-ISR models with fine-grained customized semantics and expressive guidance to unlock semantically rich and structurally consistent reconstruction. Then, we propose the Customized Semantics Module (CSM) to supplement fine-grained semantics from the image modality and regulate the semantic fusion between multi-level knowledge to realize customization for different U-Net blocks. Besides the semantic adaptation, we identify expressive multimodal signals through pair-wise comparisons and introduce the Multimodal Signal Fusion Module (MSFM) to aggregate them for structurally consistent reconstruction. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of the method. Notably, it not only achieves state-of-the-art performance on quality-driven metrics but also remains competitive on fidelity-focused metrics, striking a balance between perceptual realism and faithful content reconstruction. |
| title | MegaSR: Mining Customized Semantics and Expressive Guidance for Real-World Image Super-Resolution |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.08096 |