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Description: 对图像进行斑点噪声的添加，用方程f=f+n*f将乘性噪音添加到图像f上，其中n是均值为零，方差为var的均匀分布的随机噪声。-Image speckle noise addition, the equation f = f+ N* f will be added to the multiplicative noise on the image f, in which n is zero mean, variance var of the uniform distribution for the random noise.
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Size: 1024 |
Author: 刘昊天 |
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Description: OTSU Gray-level image segmentation using Otsu s method.
Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes
by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection
Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern.
9:62-66 1979). Thresholds are computed to maximize a separability
criterion of the resultant classes in gray levels.
OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the
corresponding Iseg is therefore a binary image. The pixel values for
Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ...
[Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability
criterion within the range [0 1]. Zero is obtained only with images
having less than n gray level, whereas one (optimal value) is obtained
only with n-valued images.- OTSU Gray-level image segmentation using Otsu s method.
Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes
by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection
Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern.
9:62-66 1979). Thresholds are computed to maximize a separability
criterion of the resultant classes in gray levels.
OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the
corresponding Iseg is therefore a binary image. The pixel values for
Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ...
[Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability
criterion within the range [0 1]. Zero is obtained only with images
having less than n gray level, whereas one (optimal value) is obtained
only with n-valued images.
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Size: 2048 |
Author: ltrko9kd |
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Description: ZNCC Normalized cross correlation
m = zncc(w1, w2)
Compute the zero-mean normalized cross-correlation between the two
equally sized image patches w1 and w2. Result is in the range -1 to 1, with
1 indicating identical pixel patterns.-ZNCC Normalized cross correlation
m = zncc(w1, w2)
Compute the zero-mean normalized cross-correlation between the two
equally sized image patches w1 and w2. Result is in the range-1 to 1, with
1 indicating identical pixel patterns.
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Size: 1024 |
Author: mahmoud |
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Description: 提出了基于Contourlet变换的数字图像水印算法。与小波变换不同的是，Contourlet变换采用类似于线段
（contour segment）的基得到一种多分辨、局部化、方向性的图像表示。水印信号通过基于内容的乘性方案加载
到 Contourlet 变换系数。在采用零均值广义高斯分布拟合 Contourlet 变换系数的基础上，提出采用极大似然估计
实现水印的盲检测。依据 Neyman-Pearson 准则，在给定虚警率的情况下对判决准则进行了优化。实验结果表明
在保证水印隐蔽性的前提下，水印对常见的信号处理手段以及几何变换具有很好的稳健性-Contourlet transform is proposed based on digital image watermarking algorithm. The difference is that with the wavelet transform, Contourlet transformation similar to that of line (contour segment) of the base by a multi-resolution, localization, directional image representation. Watermark-based content of the program is loaded into Contourlet by transform coefficients. The introduction of zero-mean generalized Gaussian distribution fitting Contourlet transform coefficient based on maximum likelihood estimation proposed by the blind watermark detection. Based on Neyman-Pearson criterion, the false alarm rate in a given case to the sentencing guidelines were optimized. The results show that the watermark hidden in the guarantee under the premise of the watermark signal processing means of the common geometric transformations and is robust
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Size: 134144 |
Author: betty |
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Description: 现有三幅图像，图像1是包装瓶中的气泡图片，图像2是印刷电路板的元器件焊接质量检查图，图像3是封装后的胶囊状药品。实验要求从这三幅图片中，
1. 选择一副图像，并叠加零均值高斯噪声，分别利用均值滤波和中值滤波器对该有噪图像进行滤波，显示滤波后的图像，比较两滤波器的滤波效果。
2. 选择一副图像，并叠加椒盐噪声，分别用均值滤波器，中值滤波器对该图像进行滤波，比较滤波器的滤波效果。
3. 选择一幅图像，分别利用Laplacian算子和Sobel算子对图像进行锐化操作，比较锐化的效果。-Three images, image packaging bottle of bubbles picture, image 2 printed circuit board components, welding quality inspection, the image is packaged in capsule form drugs. Experiments from these three pictures, select an image, and superimposed on the zero-mean Gaussian noise, respectively, the mean filter and median filter the noisy image filtering, display the image after the filtering, comparison of two filtering filtering effect of the device. 2 Select an image, and superimposed on the salt and pepper noise, respectively, the mean filter, median filter to the image filter, more filter filtering effect. Select the image, respectively, using the Laplacian operator and Sobel operator to image sharpening operation, more sharpening effect.
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Size: 199680 |
Author: 李新文 |
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Description: 对图像进行斑点噪声的添加，用方程f=f+n*f将乘性噪音添加到到图像f上，其中n是均值为零，方差为var的均匀分布的随机噪声。
-Image spots of noise, multiplicative noise is added to the equation f = f+n* f to the image f, where n is zero mean, variance var, uniformly distributed random noise.
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Size: 1024 |
Author: xlli |
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Description: Add Gaussian white noise (mean=0 and variance=0.05 ) to the image using
imnoise command. Now use fftshift command to put all zero frequencies in the
middle. Low-pass filter this function by applying a mask saving only the central
Fourier coefficients. Set the mask width to 255 and create the filtered image.
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Size: 18432 |
Author: ghazal |
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Description: This code is as per SPECT reconstruction By: Martin Š ámal Charles @ Regional
Training Workshop on Advanced Image Processing of SPECT Studies 19-23 April 2004.
The principle of the iterative algorithms is to reconstruct an image of a tomographic slice projections by successive
estimates. The projections corresponding to the current estimate are compared with
the measured projections. The result of the comparison is used to modify the current
estimate, thereby creating a new estimate.
The algorithms differ in the way the measured and estimated projections are compared and the kind of correction applied to the current estimate. The process is initiated by arbitrarily creating a first estimate - for example, a uniform image (all pixels equal zero, one, or a mean pixel value,…). Corrections are carried out either as addition of differences or multiplication by quotients between measured and
estimated projections.-This code is as per SPECT reconstruction By: Martin Š ámal Charles @ Regional
Training Workshop on Advanced Image Processing of SPECT Studies 19-23 April 2004.
The principle of the iterative algorithms is to reconstruct an image of a tomographic slice projections by successive
estimates. The projections corresponding to the current estimate are compared with
the measured projections. The result of the comparison is used to modify the current
estimate, thereby creating a new estimate.
The algorithms differ in the way the measured and estimated projections are compared and the kind of correction applied to the current estimate. The process is initiated by arbitrarily creating a first estimate - for example, a uniform image (all pixels equal zero, one, or a mean pixel value,…). Corrections are carried out either as addition of differences or multiplication by quotients between measured and
estimated projections.
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Size: 2048 |
Author: keyvan |
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Description: 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并对实验结果进行分析。-Were added to the salt and pepper noise (density 0.03) and Gaussian white noise (zero mean and variance 0.02) images, using three methods noising, shows the original image, increase noise and image denoising image and results analysis.
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Size: 1024 |
Author: 王丽 |
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Description: 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 image, increase noise and image denoising images
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Size: 10240 |
Author: zy |
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Description: gaborfeatures提取输入图像的Gabor特征。
它创建一个列向量，包括输入的Gabor特征
图像。特征向量归零均值和单位方差。-GABORFEATURES extracts the Gabor features of an input image.
It creates a column vector, consisting of the Gabor features of the input
image. The feature vectors are normalized to zero mean and unit variance.
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Size: 3072 |
Author: kk |
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Description: 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 PLOT(IMAGE_INTENSITY,VAR) plots the
functional relationship between noise variance and image intensity.
IMAGE_INTENSITY must contain normalized intensity values ranging from 0
to 1.
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Size: 2048 |
Author: Hoang Cuong
|
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Description: 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 relationship between noise variance and image intensity.
IMAGE_INTENSITY must contain normalized intensity values ranging from 0
to 1.
Platform: |
Size: 1024 |
Author: Hoang Cuong
|
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Description: 利用多帧图像平均法，对受零均值随机高斯噪声干扰的图像进行平滑处理(Multi-frame image averaging method for smoothing images disturbed by zero-mean random Gaussian noise)
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Size: 436224 |
Author: mhf999 |
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