Application of Wavelet Analysis in Image Matching

Linglong Tan, Fengzhi Wu, Xiaoyao Yin, Song Xue
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Abstract

Abstract. Based on the study of traditional matching methods, this paper implements a low-frequency image matching system based on wavelet transform, which is composed of wavelet preprocessing, low-frequency image extraction, and image matching. The low-frequency image after wavelet decomposition is used for matching, which can reduce the calculation time of matching. The low-frequency image still contains most of the visual information of the original image, making the matching result stable and reliable.In this system, image wavelet decomposition and matching use mature and fast algorithms. The matching is performed on low-frequency images, which makes the amount of calculation for matching very small. Using the low-frequency components of the image to match also greatly removes the interference of noise on the image matching. Since the highest proportion of high-frequency noise in the noise has been removed before the algorithm is matched, all the matching algorithms have good anti-noise ability.The matching system in this paper adopts a matching method based on low-frequency components after wavelet transform, discusses and realizes the use of low-frequency images after image wavelet decomposition to perform image matching. The experimental results show that the matching algorithm used in the article has fast calculation speed, less matching time, and certain practicability.
小波分析在图像匹配中的应用
摘要本文在研究传统匹配方法的基础上,实现了一种基于小波变换的低频图像匹配系统,该系统由小波预处理、低频图像提取和图像匹配三部分组成。采用小波分解后的低频图像进行匹配,减少了匹配的计算时间。低频图像仍然包含了原始图像的大部分视觉信息,使得匹配结果稳定可靠。在该系统中,图像小波分解与匹配采用成熟快速的算法。匹配是在低频图像上进行的,这使得匹配的计算量很小。利用图像的低频分量进行匹配,也极大地消除了噪声对图像匹配的干扰。由于在算法匹配之前已经将噪声中高频噪声的最高比例去除,所以所有匹配算法都具有良好的抗噪声能力。本文的匹配系统采用基于小波变换后低频分量的匹配方法,讨论并实现了利用图像小波分解后的低频图像进行图像匹配。实验结果表明,本文采用的匹配算法计算速度快,匹配时间短,具有一定的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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