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于 2021-03-09 发布 文件大小:1KB
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下载积分: 1 下载次数: 15

代码说明:

  维纳滤波法去除加有高斯噪声、椒盐噪声、乘性噪声(Wiener filter method to remove the addition of Gaussian noise, salt and pepper noise, multiplicative noise)

文件列表:

wc33.m
wg33.m
wgjc.m
wj33.m

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    2007-05-09 16:42:37下载
    积分:1
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    积分:1
  • CNN_Edge_extraction
    CNN图像边缘检测,是将细胞神经网络的标准版用matlab实现,比较简单易懂(CNN Image edge detection is realized on matlab,it is simple.)
    2011-07-15 18:11:01下载
    积分:1
  • PSNR_2
    此程序计算的是两幅图像的峰值信噪比,运行流畅(This program calculates the peak signal to noise ratio of the two images, run smoothly)
    2013-12-27 22:24:11下载
    积分:1
  • GeodesiActiveContour
    实现了geodesic active contour图像分割算法,用到AOS scheme and level set method(Realize the geodesic active contour image segmentation algorithm, using AOS scheme and level set method)
    2021-04-17 17:08:52下载
    积分:1
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    说明:  彩条产生程序。。。。720p需添加74.25M时钟(colorbar generation. need 74.25mhz clock if 720p gen)
    2020-06-22 06:20:01下载
    积分:1
  • Project_report
    Image Inpainting for object removal project report
    2014-01-07 18:52:08下载
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    本算法在Matlab2008b 环境下实现。包括main,san 和ssd 三个函数。 这次实现的算法并不是比较两个已经知道的点是否匹配,而是已知一个图形中的特征点,在另外一个图像中找到与其最匹配的点,匹配度用SAD 或者SSD 来度量。 main.m 是程序的入口,包括生成和读入实验数据,调用sad 函数和ssd 函数求匹配点,最后绘 制出最后的结果。sad.m 是用sad 度量方法在另一图中求解匹配点的函数实现,有3 个参数 y=sad(x,image1,image2)。其中x 是代匹配的数据,image1 是图像1 数据,image2 是图像2 数据。 从理论上分析,ssd 比sad 算法要复杂一点,经过测试,在一幅640*480 的图像中寻找10 个匹配点数据SAD 用时25.062519 秒,SSD 用时25.291432 秒。(The algorithm Matlab2008b environment to achieve. Including the main, san, and ssd three functions. The implementation of the algorithm is not the point of comparing two matches already know, but the known feature points in a graphic, an image found in the other match with the most points, matching measured with the SAD or SSD. main.m is the entry, including the generation and reading test data, call the sad ssd function evaluation functions and matching points, and finally draw the final result. sad.m measure is sad figure in another match point in the function implementation to solve, there are three parameters y = sad (x, image1, image2). Where x is the generation of matching data, image1 is the data image 1, image2 is the image 2 data. From the theoretical analysis, ssd little more complicated than the sad algorithm, tested in a 640* 480 images of 10 match points in the search for data using time 25.062519 seconds SAD, SSD with time 25.291432 seconds. )
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    2019-04-12 18:12:51下载
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    2016-03-20 15:09:10下载
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