Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders

• URL: http://arxiv.org/abs/2506.04641v1
• Date: Thu, 05 Jun 2025 05:23:10 GMT
• Title: Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders
• Authors: Qiming Hu, Linlong Fan, Yiyan Luo, Yuhang Yu, Xiaojie Guo, Qingnan Fan,
• Abstract summary: We introduce a novel diffusion-based SR framework, namely TADiSR, which integrates text-aware attention and joint segmentation decoders.<n>We propose a complete pipeline for synthesizing high-quality images with fine-grained full-image text masks.<n>Our approach substantially enhances text legibility in super-resolved images, achieving state-of-the-art performance across multiple evaluation metrics.
• Score: 14.655107789528673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The introduction of generative models has significantly advanced image super-resolution (SR) in handling real-world degradations. However, they often incur fidelity-related issues, particularly distorting textual structures. In this paper, we introduce a novel diffusion-based SR framework, namely TADiSR, which integrates text-aware attention and joint segmentation decoders to recover not only natural details but also the structural fidelity of text regions in degraded real-world images. Moreover, we propose a complete pipeline for synthesizing high-quality images with fine-grained full-image text masks, combining realistic foreground text regions with detailed background content. Extensive experiments demonstrate that our approach substantially enhances text legibility in super-resolved images, achieving state-of-the-art performance across multiple evaluation metrics and exhibiting strong generalization to real-world scenarios. Our code is available at \href{https://github.com/mingcv/TADiSR}{here}.

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