应用于视觉测量的图像超分辨率重建算法

Image super-resolution reconstruction algorithm for vision measurement

恶劣环境下的低质图像会严重影响基于视觉的位移测量效果。图像超分辨率重建有望能改善图像质量、突出目标特征以提高测量精度和可靠性,进而应用于视觉测量场景。故提出了一种关注细节特征的图像超分辨率重建算法,该算法设计了一个角点增强支路,并通过角点损失函数进行约束实现对角点信息的增强,此外增加边缘损失函数提升边缘的重建效果。实验结果表明,该算法在客观评价指标上表现优异,视觉效果上取得了更加清晰的纹理细节,设计的验证实验证明,该算法重建的边缘与角点更加准确,对目标定位有一定帮助,适用于视觉测量应用场景。

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.