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About this paper
Bingwen Hu, Heng Liu, Zhedong Zheng, Ping Liu
CLIP-SR is a multi-modal semantic enhancement framework that integrates textual semantics with visual features to generate high-quality super-resolution images, especially from severely degraded inputs at upscaling factors up to 16x. The framework effectively mitigates artifacts and semantic inconsistencies, achieving superior NIQE and PI scores on the COCO dataset while demonstrating better efficiency than diffusion-based methods.
Key insights
- Leveraging rich textual descriptions provides effective fine-grained guidance for reconstructing details and enhancing semantic consistency, particularly for highly degraded images.
- Dynamic adjustment of visual features based on textual context via affine transformations within a fusion block can overcome semantic mismatches between different modalities.
- Progressively refining image details and semantic coherence through iterative processing stages, guided by cross-modal features, leads to superior high-resolution outputs.
Summary by alphaXiv.
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