搜索资源列表
subbands
- 小波分析子程序模型和原理结构，子程序模型，-wavelet and waveletcompile
print
- This the source code for embedding watermarks in the detail subbands -This is the source code for embedding watermarks in the detail subbands
AutomaticSpectra
- This toolbox is Automatic spectral analysis for Irregular sampling/Missing data, analysis of spectral subbands, Vector Autoregressive modeling and Detection. It requires ARMASA toolbox. This toolbox can be downloaded from the Matlab Central file exch
1
- Cognitive radio frequency spectrum detection-The spectrum sensing of a wideband frequency range is studied by dividing it into multiple subbands. It is assumed that in each subband either a primary user (PU) is active or absent in a additive white
ASA
- 自动谱分析：可用于丢失/采样/子束光谱分析；矢量自动迭代，可用于建模，故障诊断；-The applications of this additional toolbox are: - Automatic spectral analysis for Irregular sampling/Missing data, analysis of spectral subbands, - Vector Autoregressive modeling and Detection [uses ARMA
tr-01-2005
- This makes it ideal for Internet/network applications—especially with large images and low bandwidths—as the image can be seen instantly on the decoding side, even with low-speed networks or image databases. The lower subbands are shown first, and mo
decomp_reconst
- Decompose image into subbands, denoise, and recompose again. fh = decomp_reconst(fn,Nsc,Nor,block,noise,parent) Javier Portilla, Univ. de Granada, 5/02 Last revision: 11/04-Decompose image into subbands, denoise, and recompose again.
decomp_reconst_full
- Decompose image into subbands, denoise, and recompose again. fh = decomp_reconst(im,Nsc,Nor,block,noise,parent,covariance,optim,sig) covariance: are we considering covariance or just variance? optim: for choosing between BLS-GSM (opti
decomp_reconst_W
- Decompose image into subbands, denoise using BLS-GSM method, and recompose again. fh = decomp_reconst(im,Nsc,filter,block,noise,parent,covariance,optim,sig) im: image Nsc: number of scales filter: type of filter used (see namedFilt
decomp_reconst_WU
- Decompose image into subbands (undecimated wavelet), denoise, and recompose again. fh = decomp_reconst_wavelet(im,Nsc,daub_order,block,noise,parent,covariance,optim,sig) im : image Nsc: Number of scales daub_order: Order of the daubec
wavelet
- decompose the input images into subbands
scramble
- These routines scramble an audio file by moving around short, overlapping windows within a local window. They can be used to create new versions of existing recordings that preserve the spectral content over longer time scales, but remove structure a
lpc-distrotion
- This file computes the distortion of speech files regarding to lpc model in different subbands.
ADAPTIVE-IMAGE-FUSION-ALGORITHM
- 针对低可见光图像和红外图像的特点,提出一种基于DT-CWT的自适应图像融合算法.该算法具有好的平移不变性和方向选择性,更适合于人类视觉.先对源图像作双树复小波变换,充分考虑各尺度分解层的系数特征,对 低通子带引入免疫克隆选择,根据统计评价准则定义亲和度函数,自适应获得最优融合权值 对高通子带则根据人类视觉特性定义局部方向对比度,并作为融合准则,突出和增强了各源图像的对比度与细节信息.实验结果表明: 与基于小波的融合结果相比较,本文的融合算法自适应性和鲁棒性更强,较好地保护和显示了源图像中
subbands
- 图像经过小波变换之后提取子带系数源代码在MATLAB中的实现-subbands in matlab
2
- his paper presents a reversible data hiding method based on wavelet spread spectrum and histogram modification. Using the spread spectrum scheme, we embed data in the coefficients of integer wavelet transform in high frequency subbands. The pse
61IC_H4231
- PAV (H265) 是 音视频 压缩解压 协议，非常不同于H264/MPEG4，ZPAV (H265) 的基本算法 是 小波，多级树集合群，广义小波，数学形态小波，...... ZPAV (H265) 基本算法 ： 1，图象与声音分解与合成 ：小波 ； 2，图象与声音前处理 ：小波子带零交叉降噪，目标纹理处理，语音处理 ； 3，速率控制 ：小波子带熵速率控制 ； 4，量化与反量化 ：小波子带熵量化与反量化 ； 5，低频分量和高频分量的降维 ：小波子带邻域交
1D_Gabor_transform
- 这个MATLAB软件包实现了小波在一维Gabior样转换的文件中所述： [1] K.N.乔杜里，M.，王凤琴，“建设希尔伯特变换的双 波基和Gabor变换，“对信号的IEEE交易 加工，第一卷。 57，没有。 9，3411-3425，2009年9月。 作者：K.N.乔杜里。 模块如下： --------------------------- 1。 demo.m 一个简单的例子演示软件的使用。 2。 autcorr.m
Pudn
- A filter bank structure that can deal effectively with piecewise smooth images with smooth contours, was proposed by Minh N Do and Martin Vetterli. The resulting image expansion is a directional multiresolution analysis framework composed of contour