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

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 article propose a semantic-guided global-local collaborative network (SGGLC-Net) for lightweight SISR. Our SGGLC-Net leverages semantic priors extracted from a pretrained 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 nonlocal interactions for improved detail rendition, we propose a global-local collaborative module (GLCM), 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 peak-signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values across multiple benchmark datasets, demonstrating higher performance with the multiadds 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 <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/fanamber831/SGGLC-Net</uri>.