xierbotebianhuan

结合离散小波变换(DWT)技术，利用DWT与HVS相结合的良好特性，探索和建立了一种新的图像质量客观评价方法(taking advantage of the similarities of discrete wavelet transform (DWT) with HVS，a new methods for integrated image quality assessment)

对雷达信号进行预处理。输入设定的LFM信号，进行数字正交解调以及匹配滤波最后是相参积累。从而得到LFM信号频谱图，解调后的频谱，想干积累输出等结果。 (The radar signal preprocessing. Input set of LFM signal, digital quadrature demodulation and matched filtering the last coherent integration. LFM signal spectrum, the demodulated spectrum accumulated wanted output results.)

gabor二维滤波器，用与表情识别！非常好用！做人脸识别，表情识别的朋友可以下载！(Facial expression recognition based on Gabor wavelets tranform!It is very useful to the people who research facial expression !)

LeGall(5，3)小波滤波器，其计算复杂度小、存储开销低、压缩效率高，已被JPEG2000标准采用。提升小波实例c语言实现(Lifting Wavelet Example C Language Implementation)

基于小波变换的图像拼接。 本程序利用小波变换的原理实现图像的上下，左右以及斜方向的无缝拼接(Wavelet-based image stitching. The procedure to achieve the principle of wavelet transform image from top to bottom, left and right oblique direction and seamless)

用matlab编写的提升小波算法，应用于图像处理，很好用(Matlab prepared using lifting wavelet algorithm used in image processing, very good use)

shearlet图像融合的源代码，速度很快，做毕业设计的可以参考(shearlet fusion source code, very fast, do graduate design can refer to)

基于第二代小波的非抽样小波变换应用程序，可以用来小波转换(Non-sampling based on second generation wavelets, wavelet transform application)

小波变换(wavelet transform，WT)是一种新的变换分析方法，它继承和发展了短时傅立叶变换局部化的思想，同时又克服了窗口大小不随频率变化等缺点，能够提供一个随频率改变的"时间-频率"窗口，是进行信号时频分析和处理的理想工具。它的主要特点是通过变换能够充分突出问题某些方面的特征，能对时间(空间)频率的局部化分析，通过伸缩平移运算对信号(函数)逐步进行多尺度细化，最终达到高频处时间细分，低频处频率细分，能自动适应时频信号分析的要求，从而可聚焦到信号的任意细节，解决了Fourier变换的困难问题，成为继Fourier变换以来在科学方法上的重大突破。(Wavelet transform (WT) is a new transform analysis method. It inherits and develops the idea of localization of short-time Fourier transform and overcomes the shortcomings that window size does not vary with frequency. It can provide a frequency-dependent The Time-Frequency window is ideal for time-frequency analysis and processing of signals. Its main feature is that by transforming some features that can fully highlight some aspects of the problem, the localization of the time (space) frequency can be analyzed, and the signal (function) can be gradually and multi-scale refined by the scaling operation, finally reaching the high frequency Time subdivision, frequency subdivision at low frequencies, can automatically adapt to the requirements of time-frequency signal analysis, which can focus on any details of the signal to solve the difficulties of Fourier transform has become a major breakthrough in the scientific method since Fourier transform.)

由加州理工大学教授开发的经验小波变换算法程序包，经检验比小波效果更好(California Institute of Technology developed by Professor experiences wavelet transform algorithm package, the test better than wavelet effect)