登录
首页 » matlab » denoise

denoise

于 2019-06-06 发布 文件大小:2082KB
0 248
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

  • picture
    两幅图像匹配,可以实现图片的缩放,旋转,灰度变换,压缩包内含有图片文件(picture matching)
    2012-05-16 18:50:49下载
    积分:1
  • Scratch test
    Haclon的源码,适用于金属表面的划痕检测(Haclon source code, suitable for metal surface scratch detection)
    2018-01-11 09:04:48下载
    积分:1
  • phog
    phog算法的matlab实现,hog特征用于对象识别,比如人脸识别、人体识别。(phog algorithm matlab achieve, hog characteristics for object recognition, such as face recognition, human identification.)
    2008-06-22 21:14:23下载
    积分:1
  • VC使用进度条空间
    一个简单的例子,演示如果通过VC使用进度条空间(A simple example demonstrates how to use progress bar space through VC)
    2020-06-22 08:40:01下载
    积分:1
  • beamlet
    采用beamlet变换方法,实现图像线特征提取,以此代码为基础的文章已被核心期刊录用(Beamlet transform methods used to achieve the image line feature extraction, this code is based on the core of the article has been hired Journals)
    2010-02-04 00:27:56下载
    积分:1
  • DCT
    解决图像的DCT变换与逆变换,为JEPG压缩做铺垫(To solve the image DCT transform and inverse transform, in order to pave the way for JEPG compression)
    2009-10-24 18:38:40下载
    积分:1
  • noise
    说明:  提取图像中的模式噪声,是先使用滤波器后再进行提取的(To extract the pattern noise in an image, a filter is used first and then the noise is extracted)
    2020-05-04 08:47:20下载
    积分:1
  • 图像处理之逆透视变换
    逆透视变换的数学原理,在文档中有详细的逆透视变换的矩阵推导过程,但是没有代码,opencv逆透视变换代码可以看我发的另一篇资源。。。。。。。。。。。。。。。。。。(The mathematical principle of reverse perspective transformation, in the document there is a detailed matrix rendering process of the inverse perspective transformation, but there is no code, opencv reverse perspective transformation code can see another resource I sent)
    2018-05-02 21:21:41下载
    积分:1
  • mx-maskrcnn-master
    说明:  我们提出了一个简单、灵活和通用的对象实例分割框架。我们的方法能有效检测图像中的对象,同时为每个实例生成高质量的 segmentation mask。这种被称为 Mask R-CNN 的方法通过添加用于预测 object mask 的分支来扩展 Faster R-CNN,该分支与用于边界框识别的现有分支并行。Mask R-CNN 训练简单,只需在以 5fps 运行的 Faster R-CNN 之上增加一个较小的 overhead。此外,Mask R-CNN 很容易推广到其他任务,例如它可以允许同一个框架中进行姿态估计。我们在 COCO 系列挑战的三个轨道任务中均取得了最佳成果,包括实例分割、边界对象检测和人关键点检测。没有任何 tricks,Mask R-CNN 的表现优于所有现有的单一模型取得的成绩,包括 COCO 2016 挑战赛的冠军。(Mask R-CNN code by HeKaiming)
    2020-06-17 15:20:12下载
    积分:1
  • Harr特征的几种在matlab环境下的实现
    Harr特征的几种在matlab环境下的实现(Implementation of several Harr features in Matlab environment)
    2017-09-11 15:57:53下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载