登录
首页 » matlab » KSVD-for-SAR_LOG

KSVD-for-SAR_LOG

于 2012-03-16 发布 文件大小:11793KB
0 342
下载积分: 1 下载次数: 426
我要下载

代码说明：

  用基于稀疏表示和KSVD字典学习去噪方法在对数域对SAR图像抑斑，本方法相对于一些经典的SAR图像抑斑方法，抑斑效果大大提高。(Based on the sparse representation and KSVD dictionary learning denoising method in the logarithmic domain，we despeckle SAR image, compared with some of the classic SAR image speckle suppression method, this method is much more effective.)

文件列表：

KSVD for SAR_LOG
................\baboon.raw,262144,2006-11-01

................\barb.raw,262144,1998-02-21

................\barbara.raw,262144,2006-06-28

................\barton.raw,167936,1998-02-21


................\canaletto.raw,262144,1998-02-21
................\coifman.raw,65536,1998-02-21
................\daubechies.raw,65536,1998-02-21
................\demo.m,7473,2011-10-19
................\demo2.asv,7456,2011-05-22
................\denoiseImageDCT.asv,5426,2010-11-06
................\denoiseImageDCT.m,5426,2007-01-24
................\denoiseImageGlobal.m,6046,2006-12-12
................\denoiseImageKSVD.asv,9308,2010-11-03
................\denoiseImageKSVD.m,9319,2011-04-28
................\displayDictionaryElementsAsImage.asv,3246,2007-01-25
................\displayDictionaryElementsAsImage.m,3224,2007-01-25
................\esi.m,1042,2011-05-10
................\finger.raw,262144,1998-10-14
................\fingerprint.png,184561,2002-01-08
................\fingerprint.raw,262144,1998-02-21
................\flinstones.png,174997,2001-07-14
................\func_ReadRaw.m,755,2011-01-23
................\gererateSyntheticDictionaryAndData.m,1896,2006-12-11
................\globalTrainedDictionary.mat,5749450,2005-09-21
................\goldhill.raw,262144,2006-10-23
................\house.png,34985,2002-08-29
................\KSVD.asv,12309,2010-09-03
................\KSVD.m,12380,2010-09-03
................\KSVD_NN.m,11585,2006-12-28
................\lena.png,151199,2005-09-11
................\lena.raw,262144,2006-09-08
................\lena1.png,38856,2009-10-13
................\lena_15a.bmp,263222,2010-10-12
................\lenna.raw,65536,1998-02-21
................\lincoln.raw,4096,1998-02-21
................\MOD.m,8053,2006-12-12
................\mriscan.raw,65536,1998-02-21
................\MSSIM.m,491,2011-03-06
................\my_im2col.m,631,2006-12-11
................\NN_BP.m,1105,2006-12-24
................\OMP.m,954,2007-04-29
................\OMPerr.m,1083,2006-12-11
................\peppers256.png,40181,2002-08-29
................\pointEnhance.m,1542,2010-08-20
................\R.m,812,2011-05-10
................\README.txt,4802,2006-12-28
................\sar1.bmp,59081,2010-11-18
................\sar2.bmp,196662,2010-11-18
................\sar3.bmp,66614,2010-11-18
................\sar31.bmp,259100,2010-11-18
................\sar32.bmp,343016,2010-11-18
................\sar4.bmp,23426,2010-11-18
................\sar6.bmp,93199,2010-11-18
................\sar61.bmp,94583,2010-11-18
................\sar62.bmp,123253,2010-11-18
................\small.jpg,41504,2011-01-10
................\SSIM.m,625,2011-03-06
................\washington.bmp,348660,2010-11-18
................\xsarmodel1.bmp,1337302,2010-11-18

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

  • restore-dirt-rain
    图像处理,图像去雨滴、尘土，达到图像恢复的效果，还原图像。(image derain)
    2014-06-04 17:19:31下载
    积分：1
  • matlab图像运算
    最简单的进行两图像的加减和异或，同或，运算。(The simplest operation is to add and subtract two images, and to add and subtract two images.)
    2019-02-27 15:56:16下载
    积分：1
  • ostu
    ostu图像分割阈值算法,对Ostu图像分割最优阈值进行优化处理，极大缩短了搜索图像阈值计算时间，与传统的枚举法Otsu方法相比,在计算时间上具有显著的优点。(ostu threshold image segmentation algorithm for image segmentation Ostu optimal thresholds to optimize the processing, greatly reduces the search image thresholding computation time, with the traditional enumeration method Otsu methods, in the calculation of time has significant advantages.)
    2021-03-01 11:09:34下载
    积分：1
  • EZW
    此代码是小波零树算法的matlab实现程序，子程序较为齐全，包括最后计算压缩图像的峰值信噪比和压缩效果的源码。(This code is the wavelet zero-tree algorithm matlab implementation procedures, subroutines are more complete, including the final calculation of the peak signal to noise ratio and the compressed image compression source.)
    2009-10-23 09:56:02下载
    积分：1
  • Remark-WatershedSegmentation
    说明：  性能上比经典分水岭有很大的提高。特别对于自然图像的分割，效果特别好。(Performance than the classical watershed have greatly improved. Especially for natural image segmentation, the effect particularly good.)
    2008-11-06 17:19:04下载
    积分：1
  • svm
    SVM 用于基于块划分特征提取的图像分类源码(SVM for feature extraction block-based image classification into source)
    2013-10-18 16:04:59下载
    积分：1
  • FCMs
    以标准化fcm分割为基础的，结合空间隶属度约束的改进算法(Fcm divided based on a standardized, combined with space constraints improved algorithm membership)
    2015-05-14 20:39:43下载
    积分：1
  • image-segmentation
    图像分解算法，四种方法可选，包括threshold, K means threshold, region growing以及gradient based segmentation(Image decomposition algorithm, four methods available, including the threshold, K means threshold, region growing and gradient based segmentation)
    2013-07-12 07:46:20下载
    积分：1
  • 3
    采用考虑湍流内外尺度影响的退化模型来恢复大气湍流降质图像，运用包含了湍流内外尺度影响的波结构函数、折射率谱以及薄透镜成像的退化函数导出了新的退化模型。(A new degradation model includling the influence of outer-and inner-scale of turbulence is presented to restore atmospheric turbulence degradation images. )
    2014-01-22 16:49:22下载
    积分：1
  • K-Means PCA降维
    K-Means算法,不要求建立模型之后对结果进行新的预测，没有相应的标签，只是根据数据的特征对数据进行聚类。主成分分析降维对数据进行可视化操作,对features进行降维.(K-Means algorithm does not require the establishment of the model after the new prediction of the results, there is no corresponding tag, but only on the characteristics of data clustering data. The principal component analysis reduces the dimension, carries on the visualization operation to the data, reduces the dimension to the features.)
    2017-06-22 22:05:55下载
    积分：1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载

热门标签

Visual C++ matlab C/C++ Java Others Unix_Linux EasyLanguage易语言 Visual Basic C# PDF C++ Delphi VHDL ASP WINDOWS
热门下载

Copyright© 2019-2026 | Powered by Imdn | 浙ICP备16007336号 | IMDN交流 | 联系客服（QQ：3324461878）

本网站资源收集自网络及网站用户上传，若有侵权请邮件至（3324461878@qq.com），核实后我们会在24小时内删除，谢谢！