中图分类号： TP391
文献标识码： A
DOI： 10.19358/j.issn.2096-5133.2022.05.010
引用格式： 王亚金，吴丽君，陈志聪，等. 应用于视觉测量的图像超分辨率重建算法[J].信息技术与网络安全，2022，41(5)：66-71.

Image super-resolution reconstruction algorithm for vision measurement

Abstract： The low-quality images in harsh enviroments seriously affect the effect of vision-based displacement measurement.Image super-resolution reconstruction is expected to improve image quality and highlight target features to improve measurement accuracy and reliability, and then applied to visual measurement scenarios. This study proposes an image super-resolution reconstruction algorithm that pays attention to detailed features. The algorithm designs a corner enhancement branch, and is constrained by the corner loss function to enhance the corner information, in addition to adding edge loss function. The loss function improves the reconstruction effect of the edge. The experimental results show that the algorithm performs well in objective evaluation indexes and achieves clearer texture details in visual effects. The designed verification experiment proves that the edges and corners reconstructed by the algorithm are more accurate, which is helpful for target positioning, and is suitable for visual measurement application scenarios.

Key words : deep learning；image processing；super-resolution；detailed features；visual measurement；displacement measurement；corner extration