DWT_1D

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

curvelet变换最新工具包，含有快速离散曲波变换的所有形式（包括基于usfft和wrapping和3dfdct）。功能强大，是学习曲波变换的好帮手。(curvelet transform the latest kit, containing the fast discrete curvelet transform all forms (including those based on usfft and wrapping and 3dfdct). Powerful learning curvelet transform is a good helper.)

说明：  主要针对道路提取程序，使用二值法和形态法(Road extraction procedures, using binary and morphological methods)

说明：  实现k-l变换，代码短，效率高，适合初学者进行上机练习，原理简单，比较容易懂和实现。(Achieve kl transformation, the code short, high efficiency, suitable for beginners to practice on the machine, simple in principle, relatively easy to understand and achieve.)

提升小波变换程序， %LWT Lifting wavelet decomposition 1-D. % LWT performs a 1-D lifting wavelet decomposition % with respect to a particular lifted wavelet that you specify.(lifting wavelet transform procedures% Local Walsh Transform Lifting wavelet decomposition 1-D. % Local Walsh Transform performs a 1-D lifting wavelet decomposition% with respect to a particular lifted wavelet that you specify.)

说明：  很详细的hht编程代码，matlab亲测可用，提供了比较清晰的注释，帮助理解(Very detailed HHT programming code, matlab pro test available, provides a relatively clear note to help understand)

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)

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.)

基于小波变换的信号去噪技术，简单的一维小波去噪程序(Signal de-noising based on wavelet transform technique, a simple one-dimensional wavelet denoising procedure)

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