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wavelet
说明： 关于小波变换的一些常用函数，有关多尺度变换，希望可以帮助您(Wavelet commonly used functions, on multi-scale transformation, I hope to help you)
- 2019-03-27 20:49:30下载
- 积分：1
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xiaobo
关于小波变换的降噪处理，包括强制降噪，给定阈值降噪，默认阈值降噪等，并给出了对比图(Noise reduction processing on the wavelet transform, including mandatory noise reduction, noise reduction given threshold, default threshold noise reduction, and gives comparison chart)
- 2021-05-14 11:30:03下载
- 积分：1
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基于整数小波变换的编码
基于提升格式的整数小波变换及其编解码( And its arranges based on the promotion form integer wavelet
transformation decodes )
- 2004-06-23 22:43:57下载
- 积分：1
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denoising-matlab
基于小波变换的信号检测和去噪的matlab源程序 基于小波变换的信号检测和去噪的matlab源程序(Matlab source of the signal detection and denoising based on wavelet transform)
- 2012-04-19 09:51:05下载
- 积分：1
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DT-CWTcode
该源代码又称双树复小波变换源代码，它不仅具有Gabor变换的六个方向选择性，而且有更小的冗余度，对图像处理的同行们肯定有很大的价值，可直接下载使用。(The source code also known as the Dual-Tree Complex Wavelet Transform source code, it not only has the Gabor transform of the six direction selectivity, but also has a smaller redundancy of image processing colleagues certainly have great value, can directly download使用.)
- 2021-04-21 14:28:49下载
- 积分：1
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lwt
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.)
- 2020-11-19 22:59:42下载
- 积分：1
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小波图像一次压缩
此程序能实现基于小波变换的一次图像压缩。(the program can implement once image compressiom by wavelet-based transform
)
- 2005-09-01 23:00:24下载
- 积分：1
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Source
its c-wavelet transform
- 2014-02-05 23:16:47下载
- 积分：1
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matlab谐波分析
可导入comtrade格式录波文件，进行谐波分析(It can import COMTRADE format recording file for harmonic analysis)
- 2020-06-17 23:20:01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
