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小波变换
说明： 图像小波变换，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。(Image Wavelet Transform)
- 2020-06-18 04:40:01下载
- 积分：1
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WAVELET_JX
单分量仿真信号进行小波变换后，小波脊线的提取(After the simulation of a single component signal wavelet transform, wavelet ridge extraction)
- 2021-04-15 16:58:54下载
- 积分：1
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waveletrans
给出了小波变换的具体java实现，小波变换的java程序，共包括waveletgen.java 和transformation.java(given wavelet transform to achieve specific java, java wavelet transform procedures, it includes waveletgen.java and transformation.java)
- 2006-07-26 16:25:15下载
- 积分：1
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wavelet
单小波变换应用于图像压缩,及其应用于图像融合的MATLAB代码(Single-wavelet transform to image compression, image fusion and its application to the MATLAB code)
- 2009-06-10 19:23:07下载
- 积分：1
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vulnerable_watermark
基于TS变换（一种整数变换）所实现的脆弱数字水印方法，讨论了整数水印在数字水印中的优点(transform (a Integer Transform), the fragile digital watermarking methods, Integer discussed in the digital watermark of the benefits of watermarking)
- 2006-06-10 15:22:32下载
- 积分：1
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滚动轴承外圈故障频率，小波分析后包络变换，数据来源西储大学
滚动轴承外圈故障频率，小波分析后包络变换，数据来源西储大学
- 2022-10-25 04:35:03下载
- 积分：1
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LSB8
将图像按位平面分解，然后在最低有效为嵌入一定的信息，达到数字隐藏的作用，是最原始的数字水印技术(image caput plane decomposition, and then embedded in the lowest effective for certain information, to hide the figures, is the most primitive digital watermarking technology)
- 2006-06-06 09:51:04下载
- 积分：1
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xiaobobianhuan
基于小波变换的去雾处理程序，附有实验图片，可以运行的，希望对大家有帮助！(Based on wavelet transform to fog handler, attached to the experimental images, you can run, I hope it can help you!)
- 2015-01-25 10:59:10下载
- 积分：1
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对图像实现拉东变换
说明： 可以对图像实现拉东变换，包含了原始图像与程序，可以自行修改(Radon transform of image can be realized)
- 2019-04-08 19:34:23下载
- 积分：1
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BCS-SPL-1.5-new
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
- 2020-11-23 19:29:34下载
- 积分：1
