登录
首页 » matlab » denoise

denoise

于 2019-06-06 发布 文件大小:2082KB
0 250
下载积分: 1 下载次数: 4

代码说明:

  采用自适应非局部降噪的算法对图像进行去噪处理,能够实现不同算法的降噪处理(Using adaptive non-local denoising algorithm to denoise image can achieve denoising processing of different algorithms.)

文件列表:

noise-adaptive-switching-non-local-means-master, 0 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM, 0 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\ACWMF.m, 1365 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\DBA.m, 2221 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\MAE.m, 409 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\MSE.m, 404 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\NAFSM.m, 1894 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\NASEPF.m, 3311 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\NASNLM.m, 4796 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\PSNR.m, 416 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\SNR.m, 445 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\baboon512.bmp, 263224 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\barbara128.bmp, 17464 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\barbara256.bmp, 66614 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\barbara512.bmp, 263224 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\barbara64.bmp, 5176 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\bridge128.bmp, 17464 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\bridge256.bmp, 66616 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\bridge64.bmp, 5176 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\cameraman256.bmp, 66616 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\couple256.bmp, 66614 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\dog256.bmp, 66616 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\goldhill512.bmp, 263224 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\lena128.bmp, 17464 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\lena256.bmp, 66616 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\lena512.bmp, 263224 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\lena64.bmp, 5176 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\manmade128.bmp, 17464 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\manmade256.bmp, 66616 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\manmade64.bmp, 5176 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\output, 0 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\output\0.bmp, 66614 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\output\1.bmp, 66614 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\output\1_MF.bmp, 66614 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\output\2_ACWMF.bmp, 66614 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\output\3_DBA.bmp, 66614 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\output\4_NAFSM.bmp, 66614 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\output\5_NASEPF.bmp, 66614 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\output\6_NASNLM.bmp, 66614 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\output\result.xls, 13824 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\peppers128.bmp, 17464 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\peppers256.bmp, 66616 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\peppers512.bmp, 263224 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\peppers64.bmp, 5176 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\ssim_index.m, 5960 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\start.m, 3383 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\zebra128.bmp, 23096 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\zebra256.bmp, 89144 , 2016-04-20
noise-adaptive-switching-non-local-means-master\NASNLM\zebra64.bmp, 6712 , 2016-04-20
noise-adaptive-switching-non-local-means-master\README.md, 963 , 2016-04-20

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • Desktop
    说明:  实现了图像处理中常用的对比度计算公式,和一个图像变换的画布扩大,只需要稍微修改即可用。(Image processing to achieve a common formula for calculating the contrast ratio, and an image transformation of the canvas to expand, require only a slight modification can be used.)
    2008-12-03 20:41:44下载
    积分:1
  • 车牌定位系统程序
    车牌识别程序源码,很有用的!我也是从网上下载下来的,不过我自已做了些修改!(plates source identification procedures, very useful! I also downloaded from the Internet, but I do own some changes!)
    2020-06-26 10:20:02下载
    积分:1
  • 第三章图像灰度直方图变换
    在数字图像处理中,灰度直方图是最简单且最有用的工具,可以说,对图像的分析与观察直到形成一个有效的处理方法,都离不开直方图。(in digital image processing, the histogram is the simplest and most useful tool, it can be said that the right image analysis and observation until the formation of an effective treatment method has been based on histogram.)
    2005-06-06 19:38:58下载
    积分:1
  • 小波变换c代码
    说明:  利用c语言快速实现各类小波变换,案例丰富(Using C language to realize all kinds of wavelet transform quickly, with rich cases)
    2021-03-12 17:59:25下载
    积分:1
  • canny
    说明:  外国人写的CANNY边界检测算法,代码质量很高,对初学编程很有帮助。(Foreigners write CANNY edge detection algorithm, code quality is high, very helpful for beginners programming.)
    2010-03-31 08:55:04下载
    积分:1
  • 傅里叶梅林变换参考代码
    对数及坐标变换,傅里叶梅林变换参考代码,包含多个文件(Logarithmic and coordinate transformation)
    2017-12-25 10:06:31下载
    积分:1
  • LinearClassifier_1.0
    基于Fisher线性判别基础学习器的集成分类器专门用于数字媒体的隐写分析,目前使用高维特征空间。目前,由于其良好的检测精度和较小的计算成本,它可能是设计监督分类器用于数字图像隐写分析的最常用方法。(The ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble.)
    2021-01-17 10:38:45下载
    积分:1
  • soft
    能够实现土壤水分和光照度的数据的显示与保存(Data can be achieved degree of soil moisture and light display and preservation)
    2014-06-23 20:20:53下载
    积分:1
  • StructLightCalibrate
    说明:  结构光光平面标定源码,注释详细,求出光平面在相机坐标系中的相对位置。(Structured light plane calibration source code, detailed comments, find the relative position of the light plane in the camera coordinate system.)
    2020-05-28 21:20:44下载
    积分:1
  • DDE
    红外图像细节增强仿真,其中使用双边滤波器进行图像分层。带测试源数据。(Infrared image detail enhancement, with test source data)
    2021-04-25 21:38:46下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载