DWT

对图像做二维离散小波变换，分解级数为3级，并重构图像，不直接调用Matlab的dwt和idwt函数。(pair of 2-dimensional images DWT, decomposition of the three series, and Image Reconstruction, not directly call Matlab dwt and idwt function.)

说明：  关于小波包分析的图像绘制，小波分析，三次分解，单支重构 故障信号(Image registration algorithms based on piecewise linear interpolators (spatiotemporal gradients or block matching) are analyzed to determine subpixel registration accuracy.)

说明：  小波变换算法的C语言实现，包括DWT,INDWT,提升小波，以及小波去噪的MATLAB源代码(wavelet transform algorithm in C language, including DWT, INDWT upgrade wavelet Wavelet denoising and MATLAB source code)

语音信号去噪，在MATLAB开发环境下，对含噪声的语音信号进行小波变换，实现语音增强的功能。(The speech signal denoising, wavelet transform, the noisy speech signal in the MATLAB development environment, Speech enhancement functions.)

有关小波包分解和介绍的PPT，适合于初学者快速掌握小波包分解相关问题。(the PPT of wavelet packet decomposition and the introduction , suitable for beginners to quickly master the wavelet packet decomposition.)

说明：  近年来，随着互联网的高速发展，网上的多媒体信息也急剧增加，这些多媒体信息以图像为主。如何从浩瀚的图像数据库中快速、准确地找出所需要的图像，己成为一个备受关注的研究课题。有效地组织、管理和检索大规模的图像数据成为迫切需要解决的问题。于是基于内容的图像检索(Content-Based Image Retrieval: CBIR)作为一个崭新的研究领域出现了。 本课题拟研究、分析彩色图像红、绿、蓝三基色直方图的生成、特征提取和相似度等问题，应用图像的颜色信息—三基色直方图对图像进行检索。针对基于颜色的图像检索，本文采用应用广泛的RGB颜色空间来表示图像的颜色特征，对颜色分量进行等间隔量化并形成特征矢量并对特征矢量进行归一化处理，采用图像均匀分块的方法引入图像中色彩所处的位置信息，用距离度量函数进行图像的相似性匹配。在此基础上实现了基于三基色直方图算法的检索系统。 本文的研究和实践对于促进基于内容的图像数据库检索技术的应用具有一定的参考价值和实践意义。 (With the rapid development of Internet, the multimedia information is booming. All this information is mostly images. Effective recognizing, management and searching all these images have been an emergent problem. This has led the rise of a new research and development field: Content-Based Image Retrieval (CBIR). The topics to research, analysis color images red, green and blue color histogram generation, feature extraction and the similarity of the issues, application of the color image- trichromatic histogram of the image retrieval. Based on the color against the static image retrieval, this paper application of a wide range of RGB color space to indicate the color image features, the color components, such as spacing and quantitative characteristics of a feature vector and a normalization of vector processing, using uniform image block the introduction of the method in which the color image Location information, and distance measuring function similar to the image of the match. On)

Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing

利用小波变换实现和直方图均衡化相对比的图像的增强与分割.应该比较有用.分割也采取了几种技术的对比(using wavelet transform and histogram equalization relative ratio of image enhancement and segmentation. Be more useful. Pm cutting has taken several technical contrast)

能够实现二维S变换的MATLAB程序，与Stockwell的经典程序不同(the code which can implements the 2D S-transform)

matlab计算haar 小波各级近似系数和细节系数的频率响应（CA1，CD1，CA2，CD2，CA3），并画出幅频图，便于理解小波变换的意义，(U191 u7197 u5c1 u5c1 u5Fc1 u4Fc u653 u564C u548C u7286 u8282 u7CFB u6570 u7684 u9891 u54CD u5E94 uFF08CA1 uFF0CCD1 uFF0CCA2 uFF0CCD2 UFF0CCA3 uFF09 uFF0C u5E76 u753B u51FA u5E45 u9891 u56FE uFF0C u4FBF u4E8E u7406 u89E3 u5C0F u6CE2 u53D8 u6362 u7684 u610F u4E49 uFF0C)