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tensor completion

于 2018-06-09 发布 文件大小:987KB
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  整合了不完整张量的平滑平行因子分解和最小化张量秩模型的有效选择的张量补全，用于图像处理，(The proposed method admits significant advantages, owing to the integration of smooth PARAFAC decomposition for incomplete tensors and the efficient selection of models in order to minimize the tensor rank.)

文件列表：

\multimedia_materials\Dataset\giant_missing_60.png, 94586 , 2016-08-24
\multimedia_materials\Dataset\giant_missing_70.png, 74600 , 2016-08-24
\multimedia_materials\Dataset\giant_missing_80.png, 53794 , 2016-08-24
\multimedia_materials\Dataset\giant_missing_90.png, 30718 , 2016-08-24
\multimedia_materials\Dataset\giant_missing_95.png, 17637 , 2016-08-24
\multimedia_materials\Dataset\lena_missing_60.png, 108624 , 2016-08-24
\multimedia_materials\Dataset\lena_missing_70.png, 90723 , 2016-08-24
\multimedia_materials\Dataset\lena_missing_80.png, 69569 , 2016-08-24
\multimedia_materials\Dataset\lena_missing_90.png, 42766 , 2016-08-24
\multimedia_materials\Dataset\lena_missing_95.png, 25590 , 2016-08-24
\multimedia_materials\Dataset\Original\giant.png, 131382 , 2016-08-24
\multimedia_materials\Dataset\Original\lena.bmp, 196662 , 2016-08-24
\multimedia_materials\DEMO.m, 2210 , 2016-08-24
\multimedia_materials\DEMO_color_image.m, 1599 , 2016-08-24
\multimedia_materials\Function_SPC\fold.m, 462 , 2016-08-24
\multimedia_materials\Function_SPC\FRSPC.m, 6330 , 2016-08-24
\multimedia_materials\Function_SPC\innerprod_one.m, 123 , 2016-08-24
\multimedia_materials\Function_SPC\innerprod_one_exc.m, 135 , 2016-08-24
\multimedia_materials\Function_SPC\outerprod.m, 336 , 2016-08-24
\multimedia_materials\Function_SPC\SPC.m, 6956 , 2016-08-24
\multimedia_materials\Function_SPC\sum_all.m, 40 , 2016-08-24
\multimedia_materials\Function_SPC\tensor_allprod.m, 227 , 2016-08-24
\multimedia_materials\Function_SPC\tensor_allprod_exc.m, 245 , 2016-08-24
\multimedia_materials\Function_SPC\tmult.m, 494 , 2016-08-24
\multimedia_materials\Function_SPC\unfold.m, 478 , 2016-08-24
\multimedia_materials\plotting_function\Gaussian_window.m, 217 , 2016-08-24
\multimedia_materials\plotting_function\iso_visualize.m, 311 , 2016-08-24
\multimedia_materials\plotting_function\iso_visualize_detail.m, 321 , 2016-08-24
\multimedia_materials\plotting_function\PSNR.m, 131 , 2016-08-24
\multimedia_materials\plotting_function\setpapersize.m, 159 , 2016-08-24
\multimedia_materials\plotting_function\SIR.m, 106 , 2016-08-24
\multimedia_materials\plotting_function\Xaxis_string.m, 490 , 2016-08-24
\multimedia_materials\plotting_function\Yaxis_string.m, 323 , 2016-08-24
\multimedia_materials\read_me, 263 , 2016-08-24
\README.pdf, 83611 , 2016-08-24

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0 个回复

  • YUV2Image
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    2007-05-04 10:53:23下载
    积分：1
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    2012-03-16 22:26:44下载
    积分：1
  • matchHistograms
    用matlab实现彩色图像的直方图匹配，提供源代码和说明文档( histgram matching)
    2011-08-21 22:01:41下载
    积分：1
  • TUCKER-denoising
    一种新型的基于Tucker张量分解的高光谱图像降噪算法，性能优于传统的维纳滤波(a novel method based on Tucker decomposition for HSI denoising)
    2021-04-20 23:38:50下载
    积分：1
  • ICPbyJakobWilmICP
    实现了Jakob Wilm & Hans Martin提出的ICP算法，配准两片点云。(The ICP algorithm proposed by Jakob Wilm & Hans Martin is used to register two point clouds.)
    2021-01-28 14:38:41下载
    积分：1
  • dark_channel_new
    单幅图象的暗原色先验去雾改进算法，能够很好地改进天空或明亮部分色彩失真问题，matlab代码注解详细，能很好地帮助理解算法过程。(improvement of a single image dehazing algorithm using dark channel prior,solving the problem of color distortion of sky and bright part!Having a good explain of the code,which could be helpful to understand the processing of the algorithm! )
    2012-10-25 09:20:55下载
    积分：1
  • Data Acquisition
    下面给出了48项泽尼克多项式，外加一项常数项。需要注意的是，读者并不需要严格按照下文所示的顺序排列这些泽尼克项，实际上在不同的应用和机构会采用不同的排列顺序。 表中的#0项是个常数或者说是平移项（piston term），这一项的系数也代表了平均光程差；而#1和#2项分别是x和y方向的倾斜项（tilt terms），#3代表了聚焦，因此，#1到#3项代表了波前的高斯或者近轴特性；#4和#5项代表了像散和离焦，#6和#7项代表彗差和倾斜，而#8项代表了3级像差和离焦，也就是说#4到#8项为3级相差项；同样地，#9到#15项代表了5级像差，而#16到#24项代表了7级像差，#25到#35项代表了9级像差，#36到#48项代表了11级像差。(Here are 48 Zernike polynomials, plus a constant term. It is important to note that the reader does not need to strictly arrange the Zernike entries in the order shown below, but in fact different orders of arrangement are applied to different applications and institutions. Table #0 is a constant or a shift (piston term), a coefficient of this term also represents the average optical path difference; while #1 and #2 are inclined X and Y direction (tilt terms), #3 represents the focus, therefore, #1 to #3 on behalf of the wavefront. Gauss or paraxial properties; #4 and #5 represent the astigmatism and defocus, #6 and #7 represent coma and tilt, and #8 represents the 3 order aberration and defocus, that is to say #4 to #8 is 3 level difference; similarly, #9 to #15 on behalf of the 5 order aberration #16 and #24, to represent the 7 level #25 to #35 aberration, on behalf of the 9 #36 to #48 aberration, on behalf of the 11 order aberration.)
    2017-10-09 16:29:16下载
    积分：1
  • ihcqb
    图象的处理,Image processing Image processing(Image processing, Image processing Image processing)
    2017-04-29 18:11:56下载
    积分：1
  • Texture-Image-Segmentation-
    纹理图像分割matlab程序，附带测试纹理图像图像，另外还有一篇高质量相关英文论文和一篇中文论文，供研究纹理分割的同学学习。(Texture image segmentation matlab, the supplementary test texture image image, in addition to a high quality related English papers and a Chinese paper, for research students texture segmentation study. )
    2012-03-13 15:02:06下载
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  • 图片裁剪
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    2020-06-20 18:40:02下载
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