Incorporating Uncertainty-Guided and Top-k Codebook Matching for Real-World Blind Image Super-Resolution

• URL: http://arxiv.org/abs/2506.07809v1
• Date: Mon, 09 Jun 2025 14:37:58 GMT
• Title: Incorporating Uncertainty-Guided and Top-k Codebook Matching for Real-World Blind Image Super-Resolution
• Authors: Weilei Wen, Tianyi Zhang, Qianqian Zhao, Zhaohui Zheng, Chunle Guo, Xiuli Shao, Chongyi Li,
• Abstract summary: Codebook-based real image super-resolution (SR) has shown promising results in real-world applications.<n>Existing methods face two major challenges: inaccurate feature matching with the codebook and poor texture detail reconstruction.<n>We propose a novel Uncertainty-Guided and Top-k Codebook Matching SR framework, which incorporates three key components.
• Score: 36.692648646633685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in codebook-based real image super-resolution (SR) have shown promising results in real-world applications. The core idea involves matching high-quality image features from a codebook based on low-resolution (LR) image features. However, existing methods face two major challenges: inaccurate feature matching with the codebook and poor texture detail reconstruction. To address these issues, we propose a novel Uncertainty-Guided and Top-k Codebook Matching SR (UGTSR) framework, which incorporates three key components: (1) an uncertainty learning mechanism that guides the model to focus on texture-rich regions, (2) a Top-k feature matching strategy that enhances feature matching accuracy by fusing multiple candidate features, and (3) an Align-Attention module that enhances the alignment of information between LR and HR features. Experimental results demonstrate significant improvements in texture realism and reconstruction fidelity compared to existing methods. We will release the code upon formal publication.

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