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。

这是关于小波变换的电子书，高清，最新版本，经典著作，英文版

Pascal Getreuer 的 wavelet transforms in matlab ，找了好久终于找到了，分享出来

利用小波分析技术对生物弱电--脑电信号进行预处理，降噪，出来结果比较理想

（根据维基百科，截至 2012 年 8 月 16 日） Morlet小波（或Gabor小波）是由复指数（载波）乘以高斯窗（包络）组成的小波。 该小波与人类的听觉和视觉感知密切相关。

MATLAB陡度代码analytic_wavelet 使用广义Morse小波将JM Lilly的用于脊线和元素分析的代码翻译成python。 原始的jLab存储库可在以下位置找到： Lilly，JM（2019），jLab：Matlab的数据分析包，版本1.6.6 ,. 请注意，此存储库不会重新实现jLab中的所有代码，而只是重新实现我感兴趣的部分。它也不是直接实现的。 我已经对代码进行了重组，以使API更具描述性，并且对python / numpy更加友好。 我还用内置的numpy和scipy函数替换了自定义函数，在我看来，我可以并且已经将内存布局更改为在Python中更高效（因为numpy是行占主导地位的，而MATLAB是列占主导地位的） 。 这意味着时间轴通常是我代码中的最后一个轴，但是是jLab中的第一个轴。 可以进一步简化使用更多内置的numpy和scipy代码。 从jLab中的函数到analytic_wavelet中的函数/方法的粗略映射。 并非所有的API都能准确映射 实验室 analytic_wavelet 全反式 analytic_transform 机构 振幅 瞬时频

超分辨率matlab代码小波域样式转移在单图像超分辨率中实现有效的感知失真权衡 ICCV 2019口头报告可以从以下链接下载不同数据集的结果： 这是ICCV论文“在单图像超分辨率中有效实现感知失真权衡的小波域样式转换”的实现。 第一步，您需要运行SWT.m来生成LL子带和六个高频子带。 第二步，请将上面生成的高频子带分别复制到Content和Style文件中，然后运行command.sh进行小波域风格传输。 可以从下载VGG文件，然后将其放在pre-trained_model文件中。 在第三步骤中，通过VDSR网络进一步增强了在第一步骤中生成的LL子带。 注意，需要针对不同的小波滤波器对网络进行重新训练。 所提供的模型仅适用于哈尔过滤器。 在第四步中，通过运行ISWT.m，使用逆SWT将生成的LL和高频子带重新组合为图像。 样式传送软件代码基于进行了修改。 NRQM分数是使用PIRM挑战提供的matlab代码计算的。

从国外大学网站下的，包括雷恩众多最新的算法。。

小波分析领域经典书籍,ten lectures on wavelet 中文版，小波分析领域权威Daubechies著作。