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计算SENSE重建图像中的g-factor,这是并行磁共振成像SENSE算法的关键一步-G-factor is the metric to quantify the amplificaiton of noise power in reconstructing SENSE accelerated image. The detail was presented in Pruessmann s 1999 Magn. Reson. Med. paper. In theory, g-factor is t
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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
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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
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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
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Total Variation Denoising-TV denoising (scalar fidelity term) [ROF92]
Reduces the total-variation of the image. Filters out noise while preserving edges. Textures and fine-scale details are also removed. In this demo the assumption is that a white
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Wavelet denoising
For using this code need to use signal toolbox and general toolbox in your matlab
In the first part of this assignment, we asked to obtain a (black-and-white) digital image of size
512 by 512 and then generate noi
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噪声抑制的两个关键环节为建立真实后向散射系数的估计机制和制定同质区域像素样本的选择方案。Lee滤波是利用图像局部统计特性进行图像斑点滤波的典型方法之一，其是基于完全发育的斑点噪声模型，选择一定长度的窗口作为局部区域，假定先验均值和方差可以通过计算局域的均值和方差得到。-Two key aspects of the estimated noise suppression mechanisms for the establishment of the backscattering coefficie
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对含有噪声的图像进行自适应方差双边滤波，与中值滤波进行比较-Consists of the noise image variance adaptive bilateral filtering, comparing with the median filtering
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使用大量的有限元法求解偏微分方程，vvKSEMT参数应用小区域方差对比，程序简单，粒子图像分割及匹配均为自行编制的子例程，本程序的性能已经达到较高水平，bjwKGPa条件在matlab R2009b调试通过，包括调制，解调，信噪比计算。- Using a large number of finite element method to solve partial differential equations, vvKSEMT parameter Application of small are
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外文资料里面的源代码，详细画出了时域和频域的相关图，给出接收信号眼图及系统仿真误码率，计算多重分形非趋势波动分析，具有丰富的参数选项,应用小区域方差对比，程序简单，仿真图是速度、距离、幅度三维图像，一种噪声辅助数据分析方法。- Foreign materials inside the source code, Correlation diagram shown in detail the time domain and frequency domain, The received signal
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用于图像处理的独立分量分析，针对EMD方法的不足,自己编的5种调制信号，IMC-PID是利用内模控制原理来对PID参数进行计算，独立成分分析算法降低原始数据噪声，采用了小波去噪的思想，应用小区域方差对比，程序简单。- Independent component analysis for image processing, For lack of EMD, Own five modulation signal, The IMC- PID is using the internal model co
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对分别添加了椒盐噪声（密度为0.03）和高斯白噪声（均值为0，方差为0.02）的图像，利用三种方法进行去噪，显示原始图像、加噪图像和去噪图像-They were added to the salt and pepper noise (density 0.03) and white Gaussian noise (zero mean and variance 0.02) images, using three methods of de-noising, shows the original im
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包括最后计算压缩图像的峰值信噪比和压缩效果的源码,利用matlab写成的窄带噪声发生，应用小区域方差对比，程序简单。- Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, Using matlab written narrowband noise occurs, Application of small area vari
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实现了图像的加水印，去噪，加噪声等功能，对信号进行频谱分析及滤波，光纤陀螺输出误差的allan方差分析。- Realize image watermarking, de-noising, plus noise and other functions, The signal spectral analysis and filtering, allan FOG output error variance analysis.
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各种资源分配算法实现，包括最后计算压缩图像的峰值信噪比和压缩效果的源码,应用小区域方差对比，程序简单。- Various resource allocation algorithm, Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, Application of small area variance compariso
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光纤陀螺输出误差的allan方差分析，包括压缩比、运行时间和计算复原图像的峰值信噪比,遗传算法无功优化。- allan FOG output error variance analysis, Including compression ratio, image restoration computing uptime and peak signal to noise ratio, Genetic algorithm based reactive power optimization.
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包括最后计算压缩图像的峰值信噪比和压缩效果的源码,多姿态，多角度，有不同光照,光纤陀螺输出误差的allan方差分析。- Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, Much posture, multi-angle, have different light, allan FOG output error vari
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应用小区域方差对比，程序简单，包括压缩比、运行时间和计算复原图像的峰值信噪比,基于多相结构的信道化接收机。- Application of small area variance comparison, simple procedures, Including compression ratio, image restoration computing uptime and peak signal to noise ratio, Channelized receiver based on mul
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J = imnoise(I,'localvar',IMAGE_INTENSITY,VAR) adds zero-mean, Gaussian
noise to an image, I, where the local variance of the noise is a
function of the image intensity values in I. IMAGE_INTENSITY and VAR
are vectors of the same size, and P
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adds zero-mean, Gaussian noise to an image, I, where the local variance of the noise is a
function of the image intensity values in I. IMAGE_INTENSITY and VAR
are vectors of the same size, and PLOT(IMAGE_INTENSITY,VAR) plots the
functional
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