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OepnCV基于小波变换的水下图像去噪研究

时间:2021-05-24 21:25来源:毕业论文
小波去噪的关键是如何选择阈值和如何利用阈值来处理小波系数。本文运用VC和OepnCV,选取适当的阈值和阈值函数,对一个含噪图像进行阈值去噪,与其他去噪方法得到的结果进行对比

摘要水下图像是人类了解海洋的主要途径之一。然而,水下图像在生成和传输的过程中会受到各种噪声的干扰,对信息的处理、传输和存储造成极大的影响。寻求一种既能有效地减小噪声,又能很好地保留图像边缘信息的方法,是人们一直追求的目标。67355

小波分析理论是一种新兴的信号处理理论,它在时间上和频率上都有很好的局部性,这使得小波分析非常适合于时—频分析,借助时—频局部分析特性,小波分析理论已经成为信号去噪中的一种重要的工具。利用小波方法去噪,是小波分析应用于实际的重要方面。小波去噪的关键是如何选择阈值和如何利用阈值来处理小波系数。本文运用VC和OepnCV,选取适当的阈值和阈值函数,对一个含噪图像进行阈值去噪,与其他去噪方法得到的结果进行对比,证实了小波阈值去噪法是一种实现简单、效果较好的去噪方法。

毕业论文关键词:小波变换,图像去噪,阈值,阈值函数

  

    毕业设计说明书(论文)外文摘要

   Title  Research on Underwater Image Denoising based on Wavelet Transform

Abstract

    Underwater image is one of the main ways to know about the ocean for human beings. However,in the course of its acquisition and transmission,it is always disturbed by noise ,which makes great influence to the processing, delivering and saving of information.Therefore,hunting for a method of denoising effectively and keeping the edge information simultaneously is a goal that people have been pursuing all the time. 

Wavelet analysis theory is a new theory of signal processing.It has a good locality in both frequency and time ,which makes the wavelet analysis suitable for time-frequency analysis.Wavelet analysis has played a particularly important role in denoising,due to the fact that it has the property of time-frequency analysis. Using wavelet methods to denoise is an important aspect of wavelet analysis application. The key of wavelet denoising is how to choose a threshold and how to use thresholds to deal with wavelet coefficients.In this paper,the appropriate threshold and threshold function is chosen to deal with an underwater image with noise by using VC and OepnCV.Compared with the results of using other denoising methods to deal with images,the method of threshold denoising is a good method of easy realization and effective to reduce the noise.

Keywords:Wavelet Analysis、Image Denoising、Threshold、Threshold Function

                         

                        目   次  

1    绪论 1

1.1   引言1

1.2  国内外研究历史和现状1

1.3   本文主要工作3

2   经典噪声类型及传统去噪方法 4

2.1  经典噪声类型4

2.2  传统去噪方法7

2.2.1  空域滤波7

2.2.2   频域低通滤波8

2.3   本章小结9

3    小波变换基本理论 10 

3.1   傅立叶变换  11

3.2   小波变换 11

3.2.1   连续小波变换 12

3.2.2   离散小波变换14

3.3   小波变换与傅立叶变换的比较16

3.4   多分辨率分析17 3.5   本章小结18

4   小波阈值去噪18

4.1   小波阈值去噪方法18

4.1.1   阈值处理函数  19

4.1.2   阈值选取 20

4.1.3   小波变换的尺度相关性 22 OepnCV基于小波变换的水下图像去噪研究:http://www.youerw.com/zidonghua/lunwen_75508.html

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