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Spatial-filtering-of-images
- 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。-Th
otsu
- 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
zncc
- 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.-ZN
Issppecklem
- 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.
iii
- 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 coeffi
ARTCode
- 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 tomo
3.1
- 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 im
de-noising
- 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
gabor-master
- 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
imnoise_bi
- 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
deimnoise2_bi
- 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
pinghua
- 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)