文件名称：primal dual algorithms for image processing
文件大小：11.11MB
文件格式：ZIP
更新时间：2015-02-07 16:54:03
凸优化，全变分，原始对偶算法，图像去噪，图像分割
this packet codes are about primal dual algorithms for image processing such as image denoising based on ROF model and TV-L1 and Huber ROF, image restoration like deconvolution, image zooming, image inpainting,optical flow for motion estimation and Mumford-Shah multi-label image segmentation problem. these codes are base on the following paper,"a first-order primal-dual algorithm for convex problems with application to imaging", and are organized corresponding to the structure of this paper, therefore these codes are what so-called sample codes of this paper, so they are really convenient to learn and to use. to use them, what you need to do is just to open a folder, and run the corresponding .m file, then you will collect the processing result. to understand these codes,you are recommended to read the paper first, in this case, you can get a better comprehension about these codes. and before you use them, you are also recommended first to read the instructions included in the zip packet,because in all the codes,the primal variables and dual variables are both vectorized which are different from the general situations. if you have any questions about these codes,don't hesitate to contact me via email: Pock, Thomas:pock@icg.tugraz.at Chen, Yunijn:cheny@icg.tugraz.at enjoy them,and good luck with you.
【文件预览】：
Primal_dual_algorithm[matlab codes]
----7-Motion Estimation()
--------tv_l1_motion_primal_dual.m(5KB)
--------show_flow.m(610B)
--------coarse_to_fine.m(3KB)
--------test_motion.m(1KB)
--------frame11.png(121KB)
--------computeColor.m(4KB)
--------motion.png(219KB)
--------some test images()
--------flowToColor.m(3KB)
--------writeFlowFile.m(2KB)
--------peakfilt.m(172B)
--------warping.m(1KB)
--------frame10.png(121KB)
--------illumination.png(63KB)
--------make_nabla.m(1KB)
----3-Huber-ROF()
--------compute_gap.m(409B)
--------algorithm_3.m(2KB)
--------lina_noise.jpg(86KB)
--------make_nabla.m(1KB)
----2-TV-L1()
--------algorithm_1.m(2KB)
--------louvre.jpg(80KB)
--------louvre_noise.jpg(143KB)
--------make_nabla.m(1KB)
----6-Image Inpainting()
--------rgb_TV_inpainting_primal_dual.m(2KB)
--------lina.jpg(28KB)
--------TV_inpainting_primal_dual.m(2KB)
--------rgb_Curvelet_inpainting_primal_dual.m(3KB)
--------ifdct_wrapping.m(16KB)
--------butterfly.png(614KB)
--------fdct_wrapping_window.m(751B)
--------Curvelet_inpainting_primal_dual.m(2KB)
--------make_nabla.m(1KB)
--------test_inpainting.m(981B)
--------fdct_wrapping.m(15KB)
----5-Image Zooming()
--------eye.jpg(31KB)
--------algorithm_1.m(3KB)
--------eye_2.jpg(8KB)
--------image_interp.m(399B)
--------make_A_s.m(770B)
--------s4.fig(1.09MB)
--------bicubic interpolation.fig(689KB)
--------recommended to read first.pdf(286KB)
--------eye_4.jpg(2KB)
--------make_nabla.m(1KB)
----recommended to read first.pdf(154KB)
----4-Image Deconvolution()
--------test_deconv.m(960B)
--------sunflower.png(75KB)
--------PDHG_tv_deconv_primal_dual.m(3KB)
--------tv_deconv_primal_dual.m(3KB)
--------make_nabla.m(1KB)
----8-Image Segmentation()
--------4colors.png(2KB)
--------triple-rgb_crop.png(1KB)
--------test_partition.m(2KB)
--------k_means.m(3KB)
--------butterfly.png(614KB)
--------partition_primal_dual.m(4KB)
--------kreta.png(260KB)
--------col4.png(1KB)
--------make_nabla.m(1KB)
----1-ROF()
--------algorithm_2_matrix.m(2KB)
--------compute_gap.m(830B)
--------lina.jpg(47KB)
--------AHZC.m(2KB)
--------algorithm_4_matrix.m(3KB)
--------lina_noise.jpg(86KB)
--------algorithm_1_matrix.m(3KB)
--------louvre_noise.jpg(143KB)
--------make_nabla.m(1KB)