Microdevices are widely used in the electronic industry. However， the diffraction effect， which causes misalignments in the physical and optical edges of micro devices， brings challenges to detection and measurement. To address this issue， this study combines image super-resolution reconstruction with target measurement to propose an image super-resolution reconstruction algorithm based on edge enhancement and build a corresponding measurement system. In this study， a new quality evaluation parameter is proposed for image super-resolution reconstruction， to prove the feasibility of super-resolution reconstruction in improving target measurement accuracy. Aiming at the target edge， a channel attention mechanism is also introduced into the network to enhance its ability to reconstruct the image edge. Finally， the target measurement system is designed and built， and experiments are carried out. The results show that the proposed algorithm can achieve higher peak signal to noise ratio （PSNR） and structural similarity （SSIM） values on an open dataset. In real-world measurements， this algorithm improved the limit resolution of the original measurement system by 25.9% and the target measurement accuracy by 51.6%， on average. This study provides a potential direction for the development of micro-target detection and measurement in industrial production.