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用一组完全重构的滤波器组来处理图像，可得到很高的图像重构质量。-group with a perfect reconstruction filter to the image processing, will be very high quality image reconstruction.
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过采样近似完全重构 DFT 调制滤波器组的设计。首先,分析了传递函数和
混叠传递函数的性质，给出了因果 FIR DFT 调制滤波器组的系统延迟与线性相位
分析和综合原型滤波器阶数的关系。随后，过采样近似完全重构 DFT 调制滤波器
组的设计问题被描述为一个非凸优化问题。-Near perfect reconstruction oversampled DFT modulated filter design. First, the analysis of the transfer fun
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近完美重构多相滤波器组实例，包含分析滤波器，综合滤波器，其中原型滤波器系数可调整，以及重构错误的仿真。-Near perfect reconstruction polyphase filter example, contains analysis filters, synthesis filters, which can adjust the prototype filter coefficients, and the reconstruction error is simulated.
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用Matlab实现完全重构双通道滤波器组，用matlab自带函数库实现-Using Matlab to achieve perfect reconstruction two-channel filter banks, the library comes with a matlab implementation
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MATLAB Code for Optimal Quincunx Filter
Bank Design
Yi Chen
July 17, 2006
This file introduces the MATLAB code that implements the two algorithms (i.e., Algorithms
1 and 2 in [1], or Algorithms 4.1 and 4.2 in [2]) used for the construction
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近似完全重构DFT调制滤波器组的设计与实现-Approximate perfect reconstruction DFT modulated filter banks Design and Implementation
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近似完全重构交替离散傅里叶变换调制滤波器组-Similar to the turn of perfect reconstruction DFT modulated filter banks
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Implementation of Near Perfect Reconstruction Filter Bank. This is written by MATLAB
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实现了非均匀采样线性调频信号的精确重建，主要分为两部分：实现对线性调频信号的非均匀采样；非均匀采样重建。-A perfect reconstruction algrithm for non-uniform sampling LFM signals.
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使用5阶巴特沃斯半带滤波器4通道完美重构滤波器组来处理音频数据的例子-Using the 5-order Butterworth filter with a 4-channel semi-perfect reconstruction filter banks to process audio data examples
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能实现基于vc的超小波contourlet变换（轮廓波），完全重构。-To wavelet-based ultra-vc contourlet transform , perfect reconstruction.
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使用MP将图像稀疏分解并且精确重构，其中使用2D的GA原子-MP will use the image sparse decomposition and perfect reconstruction, in which the GA using 2D atomic
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1-D信号压缩传感的实现(正交匹配追踪法Orthogonal Matching Pursuit) 测量数M>=K*log(N/K),K是稀疏度,N信号长度,可以近乎完全重构-1-D signal the realization of compressed sensing (orthogonal to match the tracking method Orthogonal Matching Pursuit) number of measurements M = K* log (N/K),
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用于实现完全重构条件下的双通道滤波器设计。低通和高通相互制约。-Used to implement a dual-channel filter design perfect reconstruction condition. Low-pass and high-pass mutual restraint.Face recognition database, including yalu MIT ORL face database. Ideal for researchers Face Recog
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这是一个双通道完全重构滤波器设计matlab源代码-This is a dual-channel perfect reconstruction filter design matlab source code
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压缩感知，又称压缩采样，压缩传感。它作为一个新的采样理论，它通过开发信号的稀疏特性，在远小于Nyquist 采样率的条件下，用随机采样获取信号的离散样本，然后通过非线性重建算法完美的重建信号。-Compressed sensing, also known as compressed sampling, compressed sensing. It as a new sampling theory, it is through the development signal sparse chara
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完全重构以及近似完全重构滤波器组设计程序。
-The goal is to design M analysis and synthesis FIR filters so that the analysis filters satisfy some frequency specifications and the filter bank(almost) meets the perfect reconstruction (PR) conditions.
The algorithm can
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4Almost Perfect Reconstruction Filter Bank for Non-redundant hosseini_ complex wavelet_06_5045[1]
-4Almost Perfect Reconstruction Filter Bank for Non-redundant hosseini_ complex wavelet_06_5045[1]
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多相滤波器组，对于实现FBMC有较大的帮助-Near perfect reconstruction polyphase filter bank demo
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Perfect Reconstruction DFT Filter Bank Real Time
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