基于改进的Harris和二次归一化互相关的量子图像拼接算法

Quantum Image Stitching Algorithm Based on Improved Harris and Quadratic Normalized Cross Correlation

针对量子图像拼接时,Harris算法需要人为设置阈值,以及图像局部相似度大导致误匹配率高的问题,提出了基于改进的Harris和二次归一化互相关(NCC)的量子图像拼接算法。在阈值设置方面,基于图像重复度高的事实,通过二值化和阈值下降统计图像子区域的量子点或环的数量以确定Harris阈值,并将其作为全图阈值。在误匹配方面,首先以小窗口进行NCC的匹配,初步筛选角点;然后在此结果上用大窗口进行第二次NCC,以降低误匹配率。实验结果表明:在量子点或环计数方面,该算法具有较好的精度和速度;在阈值设置方面,该算法将角点数量控制在合理的范围内;在匹配阶段,二次NCC的方法将误匹配率降低至4.82%~27.27%。因此,本文算法改善了量子图像拼接的可靠性和时间开销,在量子图像拼接中具有潜在的应用价值。

For the stitching of quantum images,the Harris algorithm needs to artificially set the threshold and the local similarity of the image is high,which leads to the high mismatch rate.The quantum image stitching algorithm based on improved Harris and the quadratic normalized cross correlation(NCC)is proposed.In terms of threshold setting,based on the fact that the image repeatability is high,the number of quantum dots or rings of the statistical image sub-region is determined by binarization and threshold reduction to determine the Harris threshold,and as a full-image threshold.In terms of mismatching,the NCC matching is first performed in a small window,and the corner points are initially screened.Then the second NCC is performed on the result with a large window to reduce the mismatch rate.Experimental results show that the proposed algorithm has better accuracy and speed in quantum dot or ring counting.In terms of threshold setting,the proposed algorithm controls the number of corner points within a reasonable range.In the matching stage,the quadratic NCC method reduces the mismatch rate to 4.82%-27.27%.Therefore,the proposed algorithm optimizes the reliability and time overhead of quantum image stitching,and has potential application value in quantum image stitching.