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数字图像处理中的滤波和傅里叶压缩，包括理想低通滤波器，巴特沃茨高通滤波器，高斯带通滤波器，高斯带阻滤波器，FFT图像压缩。附原图像和处理后的图像截图。-Digital image processing in filtering and Fourier compression, including the design of an ideal low-pass filter, design Batewoci high-pass filter, bandpass filter design Gaus
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首先介绍了图像中噪声的产生及与图像信号相关的噪声的分类，然后分别讨论了用于图象平滑去噪的空间域平滑处理：高斯滤波，超值像素滤波、中值滤波，和频率域平滑处理：理想低通滤波和Butterworth低通滤波处理。继而介绍了各种处理方法的程序实现，并对各种方法的去噪性能进行了比较。-this program introduce the generation ofimage noise and associated noise classification with the image signal ,
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介绍自动对焦的文章主要介绍了对焦技术探测技术包括频域分析目标函数目标函数效能验证影像评估效能之因素高斯低通滤波器参数的选取自动对焦技术以及相关实验结果与分析-Introduction Auto focus of the article introduces the focus technology, detection technology including frequency domain analysis, the objective function, the objective fu
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介绍Auto focus的文章，主要介绍了对焦技术，探测技术；包括频域分析、目标函数、目标函数效能验证、影像评估效能之因素、高斯低通滤波器参数的选取、自动对焦技术；以及相关实验结果与分析。-Introduction Auto focus of the article introduces the focus technology, detection technology including frequency domain analysis, the objective function,
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Bit Error Rate analysis of an Extended Receiver for Rectangular PAM.
The performance of a digital communication system in the presence of additive white Gaussian noise (AWGN) can be assessed by the measurement of the bit error rate (BER). The Si
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加椒盐噪声，二维高斯低通滤波器，计算二维线性滤波-Add salt and pepper noise, Gaussian low-pass filter to calculate two-dimensional linear filtering
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程序代码说明
P0301：数字图像矩阵数据的显示及其傅立叶变换
P0302：二维离散余弦变换的图像压缩
P0303：采用灰度变换的方法增强图像的对比度
P0304：直方图均匀化
P0305：模拟图像受高斯白噪声和椒盐噪声的影响
P0306：采用二维中值滤波函数medfilt2对受椒盐噪声干扰的图像滤波
P0307：采用MATLAB中的函数filter2对受噪声干扰的图像进行均值滤波
P0308：图像的自适应魏纳滤波
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参数：CV_GAUSSIAN进行滤波高斯低通滤波
-Parameters: CV_GAUSSIAN filtered Gaussian low pass filter
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对气象风场进行高斯低通滤波！带宽可以根据自己的需要适当调整，此程序适合与气象专业人员使用。-Meteorological wind field Gaussian low pass filter!
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实现两幅图像的配准，首先对图像做高斯低通滤波处理 ，然后计算互信息值，利用PWELL算法求出最优的参数，根据所得参数进行配准-To achieve the registration of two images, the first image to do the Gaussian low-pass filter, then calculate the mutual information value, use PWELL algorithm to determine the optimal pa
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使用低通滤波器，观察原始图像与以图形形式显示的高斯低通滤波器的差别，显示频谱和处理后的图像！-Using low-pass filter to observe the original image and displayed in graphical form of the difference between the Gaussian low pass filter, display the spectrum and the processed image!
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对一幅数字图像采用理想低通、Butterworth低通 、Gaussian低通滤波器进行平滑；对一幅数字图像采用理想高通、Butterworth高通 、Gaussian高通滤波器进行平滑。
-The ideal of a digital image using low pass, Butterworth low-pass, Gaussian smoothing low pass filter on a digital image using the ideal high-pass, But
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用频率域中的滤波器进行图像处理，包括理想高通滤波器和低通滤波器，butterworth高通滤波器和低通滤波器，高斯高通滤波器和低通滤波器。不调用相关函数，纯数学写。-With frequency domain filter for image processing, including the ideal high-pass filter and low pass filter, butterworth high-pass filter and low pass filters, high pa
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guassfilter-求序列x(n)(n是资料长度)的m项高斯低通滤波序列y(n)的fortran源程序/源代码-guassfilter-demand sequence x (n) (n is the data length) of m Gaussian low-pass filter sequence y (n) fortran source/source code
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用中值滤波，多维滤波，使用中心为-4,-8的拉普拉斯滤波器，高斯低通滤波，拉普拉斯滤波器进行滤波处理-Center of median filtering, multi-dimensional filtering,-4,-8 Laplace filter, the Gaussian low-pass filter, Laplacian filter to filter the
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本程序是基于vc的log算子，进行边缘检测。log算子是拉普拉斯算子和高斯低通滤波的结合，希望我的程序对大家的学习有所帮助。-This procedure is based on the vc log operator, edge detection. log operator is a combination of Laplacian and Gaussian low-pass filter, and want my program to help everyone to learn.
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利用fft2对图像傅里叶变换，在用lpfilter生成高斯低通滤波器，进行过滤再进行反变换最后显示-Use fft2 the image Fourier transform, in with lpfilter generate Gaussian low-pass filter for filtering the inverse transform last displayed
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灰度图，高斯低通滤波器处理模糊图像matlab-Grayscale, Gaussian low-pass filter processing
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要求做一个实验，对一副M*N大小的图像重复使用截频为D0的高斯低通滤波器进行滤波。假设K为滤波次数。当K足够大时，滤波结果是什么样-Asked to do an experiment, a M* N-size image re-use the cutoff frequency for the D0 Gaussian low-pass filter for filtering. Assuming K is filtering times. When K is sufficiently large,
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对一副M*N大小的图像重复使用截频为D0的高斯低通滤波器进行滤波。假设K为滤波次数。并从理论上说明当K足够大时的滤波结果及产生原因-Right an M* N size of the image is repeated using the cutoff frequency for the D0 of a Gaussian low-pass filter for filtering. Assuming K is filtering times. And filtering results when
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