CLIP-aware Domain-Adaptive Super-Resolution

• URL: http://arxiv.org/abs/2505.12391v1
• Date: Sun, 18 May 2025 12:33:00 GMT
• Title: CLIP-aware Domain-Adaptive Super-Resolution
• Authors: Zhengyang Lu, Qian Xia, Weifan Wang, Feng Wang,
• Abstract summary: This work introduces CLIP-aware Domain-Adaptive Super-Resolution.<n>It is a novel framework that addresses the challenge of domain generalization in single image super-resolution.<n>It achieves unprecedented performance across diverse domains and extreme scaling factors.
• Score: 3.272573199615535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work introduces CLIP-aware Domain-Adaptive Super-Resolution (CDASR), a novel framework that addresses the critical challenge of domain generalization in single image super-resolution. By leveraging the semantic capabilities of CLIP (Contrastive Language-Image Pre-training), CDASR achieves unprecedented performance across diverse domains and extreme scaling factors. The proposed method integrates CLIP-guided feature alignment mechanism with a meta-learning inspired few-shot adaptation strategy, enabling efficient knowledge transfer and rapid adaptation to target domains. A custom domain-adaptive module processes CLIP features alongside super-resolution features through a multi-stage transformation process, including CLIP feature processing, spatial feature generation, and feature fusion. This intricate process ensures effective incorporation of semantic information into the super-resolution pipeline. Additionally, CDASR employs a multi-component loss function that combines pixel-wise reconstruction, perceptual similarity, and semantic consistency. Extensive experiments on benchmark datasets demonstrate CDASR's superiority, particularly in challenging scenarios. On the Urban100 dataset at $\times$8 scaling, CDASR achieves a significant PSNR gain of 0.15dB over existing methods, with even larger improvements of up to 0.30dB observed at $\times$16 scaling.

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