Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-Resolution

• URL: http://arxiv.org/abs/2503.16056v1
• Date: Thu, 20 Mar 2025 11:43:55 GMT
• Title: Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-Resolution
• Authors: Wanshu Fan, Yue Wang, Cong Wang, Yunzhe Zhang, Wei Wang, Dongsheng Zhou,
• Abstract summary: Single-Image Super-Resolution (SISR) plays a pivotal role in enhancing the accuracy and reliability of measurement systems.<n>We propose a Semantic-Guided Global-Local Collaborative Network (SGGLC-Net) for lightweight SISR.
• Score: 9.666827340439669
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single-Image Super-Resolution (SISR) plays a pivotal role in enhancing the accuracy and reliability of measurement systems, which are integral to various vision-based instrumentation and measurement applications. These systems often require clear and detailed images for precise object detection and recognition. However, images captured by visual measurement tools frequently suffer from degradation, including blurring and loss of detail, which can impede measurement accuracy.As a potential remedy, we in this paper propose a Semantic-Guided Global-Local Collaborative Network (SGGLC-Net) for lightweight SISR. Our SGGLC-Net leverages semantic priors extracted from a pre-trained model to guide the super-resolution process, enhancing image detail quality effectively. Specifically,we propose a Semantic Guidance Module that seamlessly integrates the semantic priors into the super-resolution network, enabling the network to more adeptly capture and utilize semantic priors, thereby enhancing image details. To further explore both local and non-local interactions for improved detail rendition,we propose a Global-Local Collaborative Module, which features three Global and Local Detail Enhancement Modules, as well as a Hybrid Attention Mechanism to work together to efficiently learn more useful features. Our extensive experiments show that SGGLC-Net achieves competitive PSNR and SSIM values across multiple benchmark datasets, demonstrating higher performance with the multi-adds reduction of 12.81G compared to state-of-the-art lightweight super-resolution approaches. These improvements underscore the potential of our approach to enhance the precision and effectiveness of visual measurement systems. Codes are at https://github.com/fanamber831/SGGLC-Net.

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