Description: 1. 视频数据读取，读取相邻两帧中的人眼最敏感的亮度数据。 2. 用后一帧的亮度矩阵减去前一帧亮度矩阵，计算出两帧的帧差。 3. 迭代计算出帧差中的噪声的均值和标准差。 4. 根据均值和标准差滤除噪声，得到变化区域。 5. 用数学形态学运算得到对象的最终模板。-1. Video data is read, read two adjacent eyes of the brightness of the most sensitive data. 2. After a brightness of the matrix before a less brightness matrix, calculated two frames worse. 3. Iterative Frame calculated the noise mean and the standard deviation. 4. According to the mean and standard deviation of filtering noise, changes to be regional. 5. Operators using mathematical morphology to be the ultimate target template. Platform: |
Size: 2539 |
Author:王斌 |
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Description: 1. 视频数据读取，读取相邻两帧中的人眼最敏感的亮度数据。 2. 用后一帧的亮度矩阵减去前一帧亮度矩阵，计算出两帧的帧差。 3. 迭代计算出帧差中的噪声的均值和标准差。 4. 根据均值和标准差滤除噪声，得到变化区域。 5. 用数学形态学运算得到对象的最终模板。-1. Video data is read, read two adjacent eyes of the brightness of the most sensitive data. 2. After a brightness of the matrix before a less brightness matrix, calculated two frames worse. 3. Iterative Frame calculated the noise mean and the standard deviation. 4. According to the mean and standard deviation of filtering noise, changes to be regional. 5. Operators using mathematical morphology to be the ultimate target template. Platform: |
Size: 2048 |
Author:王斌 |
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Description: 随机共振相似度的Matlab计算程序，计算看出输入-输出的相似度S随着噪音的标准差的增大而不断得到改善，直到增大至一饱和值为止。-Stochastic resonance Matlab similarity calculation process, the calculation to see input- output of the similarity S as the standard deviation of the noise increases continue to improve until the saturation value increased to 1 so far. Platform: |
Size: 4096 |
Author:王清华 |
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Description: 介绍一种实用的二维条码识别算法。首先探讨了二维条码的定位与分割算法,利用Hough变换与Sobel边缘检测把条码图像从原始采集的图像中有效地分割出来 然后分析了条码图像经过光学系统的噪声模型,提出了一种计算点扩展函数标准方差的算法 采用Flourier变换自适应地选取阈值去除噪声导致的无效边界,从而得到条码的基本模块。实验结果表明,该算法具有很好的抗噪性,提高了二维条码的识别率。
-A practical two-dimensional barcode recognition algorithms. First of all two-dimensional bar code of the positioning and segmentation algorithm using Hough Transform and Sobel edge detection of the bar code images collected from the original image to effectively separate and then analyzes the barcode image through the optical system of the noise model, put forward a Calculation of point spread function of standard deviation of the algorithm uses Flourier transform adaptively select the threshold to remove noise caused by invalid border, bar code and thus be the basic module. Experimental results show that the algorithm has good noise immunity, improved two-dimensional bar code recognition rate. Platform: |
Size: 2048 |
Author:黄新 |
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Description: 介绍一种实用的二维条码识别算法。首先探讨了二维条码的定位与分割算法,利用Hough变换与Sobel边缘检测把条码图像从原始采集的图像中有效地分割出来 然后分析了条码图像经过光学系统的噪声模型,提出了一种计算点扩展函数标准方差的算法 采用Flourier变换自适应地选取阈值去除噪声导致的无效边界,从而得到条码的基本模块。实验结果表明,该算法具有很好的抗噪性,提高了二维条码的识别率。
-A practical two-dimensional barcode recognition algorithms. First of all two-dimensional bar code of the positioning and segmentation algorithm using Hough Transform and Sobel edge detection of the bar code images collected from the original image to effectively separate and then analyzes the barcode image through the optical system of the noise model, put forward a Calculation of point spread function of standard deviation of the algorithm uses Flourier transform adaptively select the threshold to remove noise caused by invalid border, bar code and thus be the basic module. Experimental results show that the algorithm has good noise immunity, improved two-dimensional bar code recognition rate. Platform: |
Size: 1024 |
Author:黄新 |
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Description: 本程序实现了短数据序列不同窗函数的welch功率谱估计，然后又求了他们的平均标准差，从而判断各个窗函数的优劣。只要稍作修改就能实现不同信噪比下的功率估计，同时也可以实现长数据序列的估计。-This procedure has a short data series of different welch window function power spectrum estimation, and then seek their average standard deviation, and thus to determine the merits and demerits of each window function. As long as we can with some slight modifications under different signal to noise ratio power estimate, but also the realization of a long data sequence can be estimated. Platform: |
Size: 1024 |
Author:wangchun |
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Description: canny检测器是很有效的边缘检测器，该函数可以实现对目标图像的边缘提取。该方法总结如下：1.图像使用带有指定标准差的高斯滤波器来平滑，以此减少噪声；2.在每一点计算局部梯度和边缘方向；3.第二步中确定的边缘点会导致梯度幅度图像中出现脊，然后追踪所有脊的顶部，并将所有不再脊顶部的像素设置为0；4.执行边缘链接-canny detector is very effective edge detector, this function can be achieved on the target image edge detection. This method is summarized as follows: 1. Image used with the specified standard deviation of the Gaussian filter to smooth out, thereby reducing the noise 2. In the calculation of each point of local gradient and edge direction 3. The second step in determining the edge points will led to the image gradient magnitude ridge appears, and then keep track of all of the top of the ridge, and ridge at the top of all the pixels are no longer set to 0 4. the implementation of the edge link Platform: |
Size: 185344 |
Author:hanyantao |
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Description: 瑞丽信道仿真
噪声信号由MATLAB函数randn(1,N)产生，它从均值为0、方差为1的正态分布中产生N个伪随机数。每次迭代时，要使用相应的标准差对噪声的幅度进行尺度变换，最后，将输入信号和噪声信号相加得到输出信号。-Ruili channel simulation noise signal by the MATLAB function randn (1, N) generated, which from the mean 0, variance for a normal distribution N generate a pseudo-random numbers. Each iteration, it is necessary to use the corresponding standard deviation of the noise amplitude scaling, finally, the input signal and the noise signal to be added to the output signal. Platform: |
Size: 1024 |
Author:eunice |
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Description:
Image Processing: Frequency Domain Enhancement
Generate noisy versions of the cameraman image by adding noise with the following characteristics:
Uniform with mean 40 and standard deviation 20
Gaussian of mean 40 and standard deviation 20
Salt and pepper noise density of 10 percent
Filter the resulting noisy images using:
1. Ideal low pass
with the following values of ro: 50, 30, and 20
2. Butterworth filters
with the following values of ro: 50, 30, and 20
3. Gaussian filters
with the following values of sigma: 30, 20, and 10
Show and label the magnitude spectra of your filters for all the above cases
Show noisy and filtered images for all the above cases
-
Image Processing: Frequency Domain Enhancement
Generate noisy versions of the cameraman image by adding noise with the following characteristics:
Uniform with mean 40 and standard deviation 20
Gaussian of mean 40 and standard deviation 20
Salt and pepper noise density of 10 percent
Filter the resulting noisy images using:
1. Ideal low pass
with the following values of ro: 50, 30, and 20
2. Butterworth filters
with the following values of ro: 50, 30, and 20
3. Gaussian filters
with the following values of sigma: 30, 20, and 10
Show and label the magnitude spectra of your filters for all the above cases
Show noisy and filtered images for all the above cases
Platform: |
Size: 5120 |
Author:engineer |
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Description:
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 namedFilters)
block: 2x1 vector indicating the dimensions (rows and columns) of the spatial neighborhood
noise: signal with the same autocorrelation as the noise
parent: include (1) or not (0) a coefficient from the immediately coarser scale in the neighborhood
covariance: are we considering covariance or just variance?
optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)
sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise)
Version using a critically sampled pyramid (orthogonal wavelet), as implemented in MatlabPyrTools (Eero).
JPM, Univ. de Granada, 3/03-
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 namedFilters)
block: 2x1 vector indicating the dimensions (rows and columns) of the spatial neighborhood
noise: signal with the same autocorrelation as the noise
parent: include (1) or not (0) a coefficient from the immediately coarser scale in the neighborhood
covariance: are we considering covariance or just variance?
optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)
sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise)
Version using a critically sampled pyramid (orthogonal wavelet), as implemented in MatlabPyrTools (Eero).
JPM, Univ. de Granada, 3/03 Platform: |
Size: 1024 |
Author:ali |
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Description: 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 daubechie fucntion used (must be even).
block: size of neighborhood within each undecimated subband.
noise: image having the same autocorrelation as the noise (e.g., a delta, for white noise)
parent: are we including the coefficient at the central location at the next coarser scale?
covariance: are we considering covariance or just variance?
optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)
sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise)
Javier Portilla, Univ. de Granada, 3/03
Revised: 11/04 - 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 daubechie fucntion used (must be even).
block: size of neighborhood within each undecimated subband.
noise: image having the same autocorrelation as the noise (e.g., a delta, for white noise)
parent: are we including the coefficient at the central location at the next coarser scale?
covariance: are we considering covariance or just variance?
optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)
sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise)
Javier Portilla, Univ. de Granada, 3/03
Revised: 11/04 Platform: |
Size: 1024 |
Author:ali |
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Description: voice) is the target signal and the
second one (woman s voice) is the interfering speaker. To contrast the
effects of an interference speaker (non-stationary noise) with a white
noise inference (stationary noise/constant power) a white noise signal
is also used. These signals are then scaled (varying SNR) to illustrate
Good intelligibility (~.6), barely intelligible (~.2), and unintelligible
(.1).
The output of the script plots the Speech intelligibility Index for 100 ms
windows over the speech signal, and displays the computed mean and standard
deviation of SII (for active speech, silence intervals were excluded).
Users can change the envelope threshold which is used to remove the
Functions required from Array Toolbox
1. intel.m
2. spectrumlevel.m
3. sii.m
4. rmsilence.m
2 Data Files required
Written by Arulkumaran Muthukumarasamy (arulkumaran@uky.edu) July 2008-voice) is the target signal and the
second one (woman s voice) is the interfering speaker. To contrast the
effects of an interference speaker (non-stationary noise) with a white
noise inference (stationary noise/constant power) a white noise signal
is also used. These signals are then scaled (varying SNR) to illustrate
Good intelligibility (~.6), barely intelligible (~.2), and unintelligible
(.1).
The output of the script plots the Speech intelligibility Index for 100 ms
windows over the speech signal, and displays the computed mean and standard
deviation of SII (for active speech, silence intervals were excluded).
Users can change the envelope threshold which is used to remove the
Functions required from Array Toolbox
1. intel.m
2. spectrumlevel.m
3. sii.m
4. rmsilence.m
2 Data Files required
Written by Arulkumaran Muthukumarasamy (arulkumaran@uky.edu) July 2008 Platform: |
Size: 2048 |
Author:tunaram |
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Description: 这是从网上整理出来的图像融合评价标准，总共有13项性能指标。包括平均梯度，边缘强度，信息熵，灰度均值，标准差(均方差MSE)，均方根误差，峰值信噪比（psnr），空间频率(sf)，图像清晰度，互信息(mi)，结构相似性(ssim)，交叉熵(cross entropy)，相对标准差。大家一起交流吧~-This is sorted out from the online image fusion evaluation criteria, there are a total of 13 performance indicators. Including the average gradient, edge strength, information entropy, gray are
Value, standard deviation (mean square error MSE), root mean square error, peak signal to noise ratio (psnr), spatial frequency (sf), image clarity, mutual information (mi), structure
Similarity (ssim), cross-entropy (cross entropy), the relative standard deviation. Exchange it with everyone ~ Platform: |
Size: 8192 |
Author:海洋 |
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Description: 图像融合中常用的评价指标（非常全面）如：平均梯度、相关系数、信息熵、交叉熵、联合熵、均方误差、互信息、信噪比、峰值信噪比、均方根误差、空间频率、标准差、均值、扭曲程度、偏差指数等等。-Image fusion evaluation (very comprehensive): average gradient, correlation coefficient, entropy, cross entropy, joint entropy, mean square error and mutual information, signal to noise ratio, peak signal to noise ratio, root mean square error, spacefrequency, standard deviation, mean, distorting the degree of deviation index. Platform: |
Size: 9216 |
Author:杨哲辉 |
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Description: 对磁共振图像进行边缘区域和图像区域的选取，并分别对两者进行均值和标准差的计算，最后得出信噪比。-For magnetic resonance images, how can we select the area of the air and the area of the image,then calculate the mean value and standard deviation value of both. Finally ,we can obtain the conclusion the signal-to-noise ratio. Platform: |
Size: 1008640 |
Author:lisha |
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Description: 首先编程生成长度为600的一个稳定信号，在此基础上再编程混叠一个标准偏差为15的高斯白噪声，得到一个含噪信号，最后用中值滤波器去噪，画出去噪效果图。-First, the length of 600 programmed to generate a stable signal, on this basis, re-programming aliasing a standard deviation of 15 Gaussian white noise, get a noisy signal, and finally with the median filter denoising, draw denoising effect diagram. Platform: |
Size: 65536 |
Author:baiyunshan |
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Description: This function generates an Additive White Gaussian Noise (AWGN) sample at every call. Its beauty lies in its simplicity! The generated sample set will have zero mean and a standard deviation of 1. Therefore, one can simply scale the output samples by a different standard deviation to generate different noise profiles. To do this scaling, simply multiply the sample with the standard deviation of your choice.
This is a very versatile function that can be used in communication system simulations and random number generation in various applications. I have used it in the past while working with channel encoder and decoder simulators. Platform: |
Size: 1024 |
Author:明 |
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Description: anisotropic diffusion
input: image to be filtered
t: radio of search window
f: radio of similarity window
k: degree of filtering
sigma: noise standard deviation
Author: Jose Vicente Manjon Herrera & Antoni Buades
Date: 09-03-2006
Implementation of the Non local filter proposed for A. Buades, B. Coll and J.M. Morel in
A non-local algorithm for image denoising
-anisotropic diffusion Platform: |
Size: 1024 |
Author:彭静 |
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Description: 这是从网上整理出来的图像融合评价标准，总共有13项性能指标。包括平均梯度，边缘强度，信息熵，灰度均值，标准差(均方差MSE)，均方根误差，峰值信噪比（psnr），空间频率(sf)，图像清晰度，互信息(mi)，结构相似性(ssim)，交叉熵(cross entropy)，相对标准差。-This is sorted out the online image fusion uation criteria, there are a total of 13 performance indicators. Including the average gradient, edge strength, information entropy, gray are Value, standard deviation (mean square error MSE), root mean square error, peak signal to noise ratio (psnr), spatial frequency (sf), image clarity, mutual information (mi), structure Similarity (ssim), cross-entropy (cross entropy), the relative standard deviation Platform: |
Size: 14336 |
Author:去额 |
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Description: 神经网络SVM实现分类，采用高斯核，标准差经过试验，最终定在0.81。训练和测试样本在1到1000之间间隔取点，训练样本取奇数，测试样本取偶数，没有噪声-SVM neural network to realize classification, USES the gaussian kernel, the standard deviation after test, final set at 0.81.Training and testing samples in the interval between 1 to 1000 points, take an odd number of the training sample, test sample taken even, no noise Platform: |
Size: 1024 |
Author:Eva |
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Search - standard deviation of the noise