PhySISR: A Self-Supervised Super-Resolution Framework for Industrial Vision With Physical Constraints

Inindustrial vision systems, image degradation due to noise, illumination variation, and optical aberrations undermines the reliability of downstream tasks, such as defect detection and parameter measurement. This article proposes PhySISR, a physics-consistent self-supervised single-image super-resolution framework tailored for industrial applications. It integrates region-aware noise simulation, Airy-disk point spread function-based degradation modeling, and structure-aware loss design to restore fine details and edge structures without any labeled data. A lightweight network is further designed to reduce parameters and inference latency, ensuring deployment efficiency. To validate the method, we construct and release SMT-ImageSet, a real-world industrial dataset captured from surface-mount equipment under diverse imaging conditions. Experiments demonstrate that PhySISR outperforms representative supervised and self-supervised methods in structural recovery, edge clarity, and downstream tasks, such as binarization and parameter extraction, showing strong applicability for practical industrial image enhancement.