Kavli Affiliate: Feng Wang
| First 5 Authors: Zhengyang Lu, Qian Xia, Weifan Wang, Feng Wang,
| Summary:
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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