MATLAB的图像增强技术研究（实验附件）
MATLAB的图像增强技术研究（实验附件）[20200101170702]
21世纪是飞速发展的信息化时代，图像作为有效的信息载体，是人们获取与交换信息的主要来源。?然而，在一般情况下，比如在摄影时由于光照条件不足或过量，会使图像过暗或过亮，光学系统的误差、相对运动、大气流动等，传输过程中各种原因的失真等，都会造成图像质量的下降，使得图像模糊不清甚至根本无法识别。图像增强是从不同的途径获取的图像，通过进行适当的增强处理，可以将原始图像处理成清晰的富含大量有用信息的可使用图像，有效地去除图像中的噪声、增强图像中的边缘或其他感兴趣的区域，从而更加容易对图像中感兴趣的目标进行检测和测量。本文采用了亮度变换，直方图均衡化，拉普拉斯变换，高斯高通变换，小波变换这五种增强算法，通过MATLAB软件作为实验平台，对不同的图像进行图像增强处理和研究。实验得到相同的增强算法对应不同的图像增强效果不同，进行图像增强时应选取合适的增强算法，改变参数使得图像中感兴趣的部分变得更为清晰，从而得到理想图像。 *查看完整论文请+Q: 351916072
关键字:图像增强,MATLAB,亮度变换,直方图均衡化,拉普拉斯变换,高斯高通变换,小波变换
目录
1 绪论·······························································································································1
1.1 研究目的及意义········································································································1
1.2 国内外研究状态········································································································2
2 图像增强方法简介·······································································································3
2.1 亮度变换····················································································································4
2.2 直方图均衡化············································································································4
2.3 拉普拉斯滤波器········································································································5
2.4 高斯高通滤波器········································································································5
2.5 小波变换····················································································································6
3 MATLAB简介··············································································································7
3.1 MATLAB的历史背景·······························································································7
3.2 MATLAB的基本概述·······························································································8
3.3 MATLAB的特点·······································································································9
4 利用MATLAB的图像增强························································································10
4.1 图像的亮度变化········································································································10
4.2 图像的直方图均衡化································································································12
4.3 图像的拉普拉斯滤波变换························································································14
4.4 图像的高斯高通滤波变换························································································15
4.5 图像的小波变换········································································································16
结论 ·································································································································18
致谢 ·································································································································19
参考文献 ·························································································································20
附录 ·································································································································21
1 绪论
1.1 研究目的及意义
图像就是物体透射或反射的光信息，通过视觉系统接受后，在人的大脑中形成的印象或认识，是自然中景物客观反映的体现。一般来说，凡是能为人类的视觉系统所感知的有形信息，或人们心目中的有形的想象都统称为图像。
图像作为一种有效的信息载体，是人类获取和交换信息的主要来源。实践表明，人类感知的外界信息，80%以上是通过视觉得到的。?但是，与此同时，一般经过图像的传送和转换处理，比如成像、复制、扫描、传输和显示等，经常会造成图像的质量下降，即图像失真。举几个例子，在摄影时，由于光照的过度或不足，会使图像过亮或过暗；光学系统的失真、大气流动、相对运动等都会使图像模糊；当然，还是传输过程中引入的各种类型的噪声等等。总之输入的图像在视觉效果和识别方便性等方面可能存在诸多问题，因此，图像处理的应用领域必然涉及到人类生活和工作的方方面面。所谓图像处理，就是通过某些数学运算对图像信息进行加工和处理，以满足人的视觉心理和实际应用需求[1]。图像增强是图像处理的一个环节，在整个图像处理中起着承前启后的重要作用。
21世纪是飞速发展的信息化时代，图像作为有效的信息载体，是人们获取与交换信息的主要来源。?然而，在一般情况下，比如在摄影时由于光照条件不足或过量，会使图像过暗或过亮，光学系统的误差、相对运动、大气流动等，传输过程中各种原因的失真等，都会造成图像质量的下降，使得图像模糊不清甚至根本无法识别。图像增强是从不同的途径获取的图像，通过进行适当的增强处理，可以将原始图像处理成清晰的富含大量有用信息的可使用图像，有效地去除图像中的噪声、增强图像中的边缘或其他感兴趣的区域，从而更加容易对图像中感兴趣的目标进行检测和测量。本文采用了亮度变换，直方图均衡化，拉普拉斯变换，高斯高通变换，小波变换这五种增强算法，通过MATLAB软件作为实验平台，对不同的图像进行图像增强处理和研究。实验得到相同的增强算法对应不同的图像增强效果不同，进行图像增强时应选取合适的增强算法，改变参数使得图像中感兴趣的部分变得更为清晰，从而得到理想图像。 *查看完整论文请+Q: 351916072
关键字:图像增强,MATLAB,亮度变换,直方图均衡化,拉普拉斯变换,高斯高通变换,小波变换
目录
1 绪论·······························································································································1
1.1 研究目的及意义········································································································1
1.2 国内外研究状态········································································································2
2 图像增强方法简介·······································································································3
2.1 亮度变换····················································································································4
2.2 直方图均衡化············································································································4
2.3 拉普拉斯滤波器········································································································5
2.4 高斯高通滤波器········································································································5
2.5 小波变换····················································································································6
3 MATLAB简介··············································································································7
3.1 MATLAB的历史背景·······························································································7
3.2 MATLAB的基本概述·······························································································8
3.3 MATLAB的特点·······································································································9
4 利用MATLAB的图像增强························································································10
4.1 图像的亮度变化········································································································10
4.2 图像的直方图均衡化································································································12
4.3 图像的拉普拉斯滤波变换························································································14
4.4 图像的高斯高通滤波变换························································································15
4.5 图像的小波变换········································································································16
结论 ·································································································································18
致谢 ·································································································································19
参考文献 ·························································································································20
附录 ·································································································································21
1 绪论
1.1 研究目的及意义
图像就是物体透射或反射的光信息，通过视觉系统接受后，在人的大脑中形成的印象或认识，是自然中景物客观反映的体现。一般来说，凡是能为人类的视觉系统所感知的有形信息，或人们心目中的有形的想象都统称为图像。
图像作为一种有效的信息载体，是人类获取和交换信息的主要来源。实践表明，人类感知的外界信息，80%以上是通过视觉得到的。?但是，与此同时，一般经过图像的传送和转换处理，比如成像、复制、扫描、传输和显示等，经常会造成图像的质量下降，即图像失真。举几个例子，在摄影时，由于光照的过度或不足，会使图像过亮或过暗；光学系统的失真、大气流动、相对运动等都会使图像模糊；当然，还是传输过程中引入的各种类型的噪声等等。总之输入的图像在视觉效果和识别方便性等方面可能存在诸多问题，因此，图像处理的应用领域必然涉及到人类生活和工作的方方面面。所谓图像处理，就是通过某些数学运算对图像信息进行加工和处理，以满足人的视觉心理和实际应用需求[1]。图像增强是图像处理的一个环节，在整个图像处理中起着承前启后的重要作用。
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