一篇非常不错的指纹识别的论文,有比较完善的指纹识别的思路，不过里面的算法可要好好琢磨哦

该程序的主要计算小波功率谱(wavelet power spectrum) 全球小波谱（global wavelet spectrum）和 scale-average time series (自己利用的时候可能里面的时间需要修改，或者参考以上的程序自己根据自己的需要进行编写)

Wavelet-Watermark(小波变换(Wavelet)实现数字水印)

小波变换及matlab原始码此配置文件旨在实现：使用小波变换的数字图像模糊检测，童杭航，李明京，张洪江 我使用了两种编程语言（matlab和C ++）来实现它。 使用Matlab源代码只需执行脚本start.m 使用C ++源代码我的代码取决于Opencv，如果您的计算机具有Opencv（> = 2.4.x），请在bash shell终端上执行命令./compile.sh main.cpp来生成执行文件“ main”，然后执行命令./main [图像]运行。

MATLAB小波软硬阈值去噪代码基于小波的去噪MATLAB代码 要运行该实现，只需运行“ project.m”文件。 将出现5张图像： 原始的“莱娜” 256x256图片（黑白） 添加了AWGN的图片 图片通过纸算法实现去噪 通过Visushrink硬阈值实现对图像进行去噪 通过Visushrink软阈值实现对图像进行去噪 相应的嘈杂的SNR和所有三个去噪的图片也将被打印在命令窗口中。 Visushrink算法的代码（用于比较）由M. Kiran Kumar实现，并通过Mathworks网站（）下载。 Lipschitz指数是由Venkatakrishnan等人通过题为“使用小波变换模量极大值（WTMM）的Lipschitz指数（LE）的测量”的方程式（9）来计算的。 （IJSER-2012年6月）。

里面有很多小波变换的源代码，直接可以运行，还有些论文，学习小波变换编程的好资料。

在信号处理中，小波变换效果受到整形参数、小波长度、中心频率、频带宽度及小波个数等参数的制约，特别是整形参数与小波中心频率及频带之间关系对小波变换起到决定性作用。在信号的实际处理中，可选取恰当的整形参数，同时采用合适的小波中心频率以避免小波变换对信号产生的遗漏和冗余，自适应连续小波变换就能达到此目的。

人脸识别[Wavelet and Neural Networks] V2：基于小波和神经网络的简单有效的人脸识别源代码。 该代码已通过AT＆T数据库进行测试，达到了97.90％的出色识别率（每个类别40个类别，5个训练图像和5个测试图像，因此总共随机选择了200个训练图像和200个测试图像，并且两者之间没有重叠训练和测试图像）。 查看更多：http://matlab-recognition-code.com/face-recognition-based-on-wavelet-and-neural-networks-matlab-code/

nicely organized and thoroughly pedagogical handbook … The book is very much 'figure driven' and they are extremely useful for illustrating the mathematics and conveying the concepts in an uncomplicated matter … an eminently readable style in motivating and explaining wavelets throughout the course … this book, written as it is in a simple, forthright, and stimulating manner with balanced blend will prove a worthy addition to the understanding of wavelet transforms across disciplines. -Guruprasad Madhavan, Stony Brook Heart Center, IEEE-EMBS The book works on two levels: it is an excellent introduction for readers new to wavelet transforms, and it is a useful source of information and some inspiration to anyone who has been working in the field for some time. … The book presents the field in an authoritative way but not too deeply. It has been designed as an overview and introduction to this dynamic area of computational analysis, and it achieves this with complete success. To conclude, I found this a thought provoking book on a fascinating subject: in fact once into the text I found it hard to put down. -Dr. K.O. Jones, Measurement+Control, Vol. 36/7, Sept. 2003 The book is extremely well put together and lavishly illustrated (as one might infer from the title). … I largely enjoyed reading this book and I strongly recommend it to anyone with an interest in wavelets, or in the general field of signal processing for that matter. -Keith Worden, Journal of Sound and Vibration, 274 (2004) 1135-1136 The book … provide[s] the reader with an overview of theory and practical applications of wavelet transforms methods, a new time-frequency decomposition tool for data analysis … The book can be recommended to the people interested above all in applications in science, engineering, medicine, finance, or elsewhere. An account of the theory of continuous and discrete wavelet transforms, with a large number of examples of their use across a wide range of disciplines, is convenient both for newcomers to the subject ad readers already acquainted with wavelets methods working in particular area of application. -Miloslav Duchori Illustrations really do help in understanding wavelet transforms. It might be taken for granted that illustrations can aid in the understanding of a subject matter as this, but there is sound logical reasoning behind this claim that makes it especially true for this particular subject matter and also a great motivation for the text. This handbook goes the distance and shows that wavelet transforms are powerful data analysis tools that readily lend themselves to intuitive visual analysis and graphical representation, both of which can be critical to correct analysis in practice. -Raymond Martin

二维连续小波变换Matlab源代码，超级宝贵的资源，无私分享。