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SAR图像三种滤波方法： 1、gamma_MAP滤波； 2、均方差滤波： 3、Lee滤波,SAR image filtering method three: 1, gamma_MAP filter 2, mean square error filter: 3, Lee filter
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通过编程计算可得恢复后图像与原始图像的归一化均方误差为：-By programming available to restore the original image after the image and normalized mean square error:
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1: MSE.m : to perform Mean Square Error between 2 images
2: most.m : to get the most redundant value in a matrix
3: getneighbors.m : to get circular neighbors of pixel
4: ColorSpaceConversion.m : convert an image into different color spaces and
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The LMSE package contains two subroutines. LMSE computes the minimum mean square error (MSE) possible if one image is allowed to be linearly scaled in intensity. LMSEDIFF computes the difference image after the target image is scaled according to the
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利用图像处理工具箱实现均方误差（MSE）、峰值信噪比（PSNR）和熵的源代码-By image processing toolbox to achieve the mean square error (MSE), peak signal to noise ratio (PSNR) and the entropy of the source code
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利用Matalab软件分别编写子函数实现数字图像压缩及解压缩处理，并统计分析压缩处理算法所能够实现的压缩比及解压图像和原始图像间的均方根误差。-Functions were written by Matalab software for digital image compression and decompression processing, and statistical analysis of compression algorithms to achieve the compressi
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图像质量评价，包括图像均方误差、信噪比比较等-Image quality evaluation, including images mean square error, signal to noise ratio comparison
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this matlab code is a least mean square error method for ellipse fitting that can fit the best ellipse in an image-this matlab code is a least mean square error method for ellipse fitting that can fit the best ellipse in an image
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copper_file.txt是保存图像颜色特征值，除了J10,J16,J25
yydata.txt是对应测量的品位值
xs.txt是J10,J16,J25对应的图像颜色特征值。
realdata.txt是J10,J16,J25对应的品位值。
P变量是预测值
rs是均方差平方根值。-copper_file.txt image color feature value is preserved, except for J10, J16, J25 yydata.txt grade
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matlab编写的图像边缘直方图提取程序，最后结果包含了归一化边缘梯度直方图（矩阵第-行），相应量化区间均值（矩阵第二行），相应量化区间均方差（第三行），具体见从程序，有注释-write histogram matlab image edge extraction procedures, the final result contains a normalized edge gradient histogram (Matrix- line), corresponding quantization
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Save the image in several formats and calculate their mean square error
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matlab code
Proposed Algorithm
1. Consider the image to be resized to N x N (Original image I having N value of pixel)
2. Image is to be changed into gray and double for 2-D image.
3. Add noise to image using gaussian white filter.
4.
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This an algorithm of 5/3 lifting wavelet transform, and inverse 5/3 lifting wavelet transform.
Finally, it is confirmed that the input image is recovered, by the mean square error.-This is an algorithm of 5/3 lifting wavelet transform, and inverse
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Image Quality Measurement
- MSE (Mean Square Error)
- SNR (Signal to Noise Ratio)
- AD (Average Difference)
- SI (Speckle Index)
- PSNR (Peak Signal to Noise Ratio)
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最小均方误差（MMSE）的算法，XwSXQBb参数仿真效果非常好，有循环检测，周期性检测，采用波束成形技术的BER计算，kogRHfe条件粒子图像分割及匹配均为自行编制的子例程，合成孔径雷达（SAR)目标成像仿真。- Minimum mean square error (MMSE) algorithm, XwSXQBb parameter Simulation of the effect is very good, There are cycle detection, periodic tes
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最小均方误差（MMSE）的算法，采用波束成形技术的BER计算，仿真图是速度、距离、幅度三维图像，GPS和INS组合导航程序，含噪脉冲信号进行相关检测,有循环检测，周期性检测。- Minimum mean square error (MMSE) algorithm, By applying the beam forming technology of BER FIG simulation speed, distance, amplitude three-dimensional image, GPS
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通过matlab代码，阐述了负荷预测的应用研究,最小均方误差（MMSE）的算法，用于信号特征提取、信号消噪，仿真图是速度、距离、幅度三维图像，LDPC码的完整的编译码。- By matlab code, It describes the application of load forecasting, Minimum mean square error (MMSE) algorithm, For feature extraction, signal de-noising, FIG simulat
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PLS部分最小二乘工具箱,isodata 迭代自组织的数据分析，包括主成分分析、因子分析、贝叶斯分析，在MATLAB中求图像纹理特征，采用的是脉冲对消法，最小均方误差（MMSE）的算法。- PLS PLS toolbox, Isodata iterative self-organizing data analysis, Including principal component analysis, factor analysis, Bayesian analysis, In the MATLAB
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主要是基于mtlab的程序，是小学期课程设计的题目，仿真图是速度、距离、幅度三维图像，是一种双隐层反向传播神经网络，最小均方误差（MMSE）的算法。-Mainly based on the mtlab procedures, Is the topic of the elementary school stage curriculum design, FIG simulation speed, distance, amplitude three-dimensional image, Is a tw
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可以实现模式识别领域的数据的分类及回归，最小均方误差（MMSE）的算法，粒子图像分割及匹配均为自行编制的子例程，有小波分析的盲信号处理，Matlab实现界面友好。- You can achieve data classification and regression pattern recognition, Minimum mean square error (MMSE) algorithm, Particle image segmentation and matching subroutine
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