搜索资源列表
OpenGLfenxingshu
- OPENGL分形树 鼠标键盘控制 增加高斯随机函数-OpenGL fractal tree mouse and keyboard control to increase Gaussian random function
matlabpro2
- 以matlab实现数字水印的生成和提取,用的是高斯随机序列生成的易碎的隐式数字水印。-to Matlab digital watermark generation and extraction, using a Gaussian random sequence generation of friable implicit digital watermarking.
GauNoise
- 高斯随机噪音程序,做理论模型数据时,一般会加高斯随机噪音,以模拟实际数据
imnoise2
- 产生均匀、高斯、椒盐、对数正态、瑞利、指数、厄兰随机噪声-Have a uniform, Gaussian, salt and pepper, Lognormal, Rayleigh, exponential, beijerland random noise
matlab1
- 产生高斯白噪声,已知期望和方差,这样就生成了一组随机数据-Generated white Gaussian noise of known expectation and variance, thus generating a set of random data
project1
- 可添加随机噪声、高斯噪声、椒盐噪声,并比较几种去噪算法的峰值信噪比-Can add random noise, Gaussian noise, salt and pepper noise, and compare several denoising algorithm PSNR
NoiseGenerator
- 本实验要求根据课本中给出的高斯噪声和椒盐噪声的概率分布的形状和参数编写两个通用程序分别给一个图像中添加高斯噪声和椒盐噪声。高斯噪声是n维分布都服从高斯分布的噪声,椒盐噪声是图像中经常见到的一种噪声是一种随机的黑点或者白点。在实验中通过它们对应的概率密度函数得到噪声分布函数进而与原图像进行叠加产生出对应的噪声图像-Textbooks in this experiment are given under the Gaussian noise and salt and pepper noise in
8psk
- 1、 产生等概率且相互独立的二进制序列,画出波形; 2、产生均值为0,方差为1的加性高斯随机噪声; 3、进行8PSK调制,画出波形; 4、进行蒙特卡罗分析; 5、解调8PSK,画出眼图。-1、Produce equal probability and independent of each other binary sequence, draw waveform 2、Generate mean 0, variance 1 of the additive Gaussian ra
restoration
- (1) 先由原始图像(任选)产生待恢复的图像;(产生方法如下:冲激函数为 ,将原始图像与冲激函数卷积产生模糊,然后再迭加均值为0,方差为8,16,32的高斯随机噪声而得到一组待恢复的图像。分别用逆滤波和维纳滤波恢复模糊后的图像。-(1) The first be the original image (optional) produces the image to be restored (generated as follows: impulse function, the original
3
- 模式识别,主要是对两维多类的高斯随机序列进行分类-PR_studio
k_means
- 实现K-means算法,高斯随机分布抽取数据 利用k-means算法实现分类-Implement K-means algorithm to extract the data using Gaussian random distribution of k-means classification algorithm
liboqichengxi
- 巴特沃斯带通数字IIR滤波器对加了高斯随机噪声和正弦噪声的语音信号进行滤波,并绘制了两滤波器滤波后的语音信号时域图和频谱图。-Butterworth band-pass digital IIR filter added Gaussian random noise and sinusoidal noise of the speech signal is filtered, and draw a diagram of two filter the filtered speech signal tim
Wavelet_IRLS
- 压缩感知CS——采用小波变换进行稀疏表示,高斯随机矩阵为观测矩阵,重构算法为ILRS算法,对256*256的lena图处理,比较原图和IRLS算法在不同采样比例(0.74、0.5、0.3)下的重构效果,并各运行50次,比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
Wavelet_OMP
- 压缩感知CS——采用小波变换进行稀疏表示,高斯随机矩阵为观测矩阵,重构算法为OMP算法,对256*256的lena图处理,比较原图和OMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果,并各运行50次,比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
Wavelet_SP
- 压缩感知CS——采用小波变换进行稀疏表示,高斯随机矩阵为观测矩阵,重构算法为SP算法,对256*256的lena图处理,比较原图和SP算法在不同采样比例(0.74、0.5、0.3)下的重构效果,并各运行50次,比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and
Wavelet_ROMP
- 压缩感知CS——采用小波变换进行稀疏表示,高斯随机矩阵为观测矩阵,重构算法为ROMP算法,对256*256的lena图处理,比较原图和ROMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果,并各运行50次,比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matr
Wavelet_SL0
- 压缩感知CS——采用小波变换进行稀疏表示,高斯随机矩阵为观测矩阵,重构算法为SL0算法,对256*256的lena图处理,比较原图和SL0算法在不同采样比例(0.74、0.5、0.3)下的重构效果,并各运行50次,比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
[kimi]Noise
- 实现了在图像中生成均匀随机噪声、高斯随机噪声和椒盐随机噪声的算法。-The code for creating uniform noise, Gaussian noise and Salt-pepper noise in image processing.
DCT_Gaosi_fenkuai
- 对256*256大小的8bit灰度lena图像进行仿真 将图像分为16*16的分块进行计算 稀疏矩阵采用DCT矩阵,观测矩阵采用高斯随机矩阵,重构采用OMP算法- 256* 256 size lena image simulation 8bit grayscale image is divided into 16 * 16 calculate block sparse matrix using DCT matrix, observation matrix using Ga
zaosheng
- 对图像分别加入高斯噪声和随机噪声,再分别用中值滤波和邻域平均方法进行滤波(The Gauss noise and random noise are added to the image, and the median filtering and neighborhood averaging are used to filter the image respectively)