CDN加速镜像 | 设为首页 | 加入收藏夹
登陆 | 会员注册
当前位置： 首页 资源下载 源码下载 数值算法/人工智能 matlab例程
收藏

文件名称:mmse_mrf_demo-1.1

  • 所属分类：
    matlab
  • 标签属性：
    [Matlab] [源码]
  • 上传时间：
    2013-07-04
  • 文件大小：
    1.16mb
  • 已下载：
    0次
  • 提 供 者：
    孙****
  • 相关连接：
    无
  • 下载说明：
    别用迅雷下载，失败请重下，重下不扣分！
修正介绍说明

介绍说明－－下载内容来自于网络，使用问题请自行百度

图像去噪-A Generative Perspective on MRFs in Low-Level Vision-A Generative Perspective on MRFs in Low-Level Vision

Markov random fields (MRFs) are popular and generic

probabilistic models of prior knowledge in low-level vision.

Yet their generative properties are rarely examined, while

application-specific models and non-probabilistic learning

are gaining increased attention. In this paper we revisit

the generative aspects of MRFs, and analyze the quality of

common image priors in a fully application-neutral setting.

Enabled by a general class of MRFs with flexible potentials

and an efficient Gibbs sampler, we find that common models

do not capture the statistics of natural images well. We

show how to remedy this by exploiting the efficient sampler

for learning better generative MRFs based on flexible potentials.

We perform image restoration with these models

by computing the Bayesian minimum mean squared error

estimate (MMSE) using sampling. This addresses a number

of shortcomings that have limited generative MRFs so far,

and le
(系统自动生成,下载前可以参看下载内容)

下载文件列表

mmse_mrf_demo/
mmse_mrf_demo/+image_patches/
mmse_mrf_demo/+image_patches/load.m
mmse_mrf_demo/+image_patches/training/
mmse_mrf_demo/+image_patches/validation/
mmse_mrf_demo/+learned_models/
mmse_mrf_demo/+learned_models/cvpr_3x3_foe.m
mmse_mrf_demo/+learned_models/cvpr_pw_mrf.m
mmse_mrf_demo/+pml/
mmse_mrf_demo/+pml/+distributions/
mmse_mrf_demo/+pml/+distributions/@discrete/
mmse_mrf_demo/+pml/+distributions/@discrete/discrete.m
mmse_mrf_demo/+pml/+distributions/@discrete/eval.m
mmse_mrf_demo/+pml/+distributions/@discrete/kl_divergence.m
mmse_mrf_demo/+pml/+distributions/@discrete/mle.m
mmse_mrf_demo/+pml/+distributions/@discrete/plot.m
mmse_mrf_demo/+pml/+distributions/@discrete/private/
mmse_mrf_demo/+pml/+distributions/@discrete/private/montecarlo.m
mmse_mrf_demo/+pml/+distributions/@discrete/sample.m
mmse_mrf_demo/+pml/+distributions/@discrete/semilogy.m
mmse_mrf_demo/+pml/+distributions/@discrete/test/
mmse_mrf_demo/+pml/+distributions/@discrete/test/test_all.m
mmse_mrf_demo/+pml/+distributions/@foe/
mmse_mrf_demo/+pml/+distributions/@foe/energy.m
mmse_mrf_demo/+pml/+distributions/@foe/eval.m
mmse_mrf_demo/+pml/+distributions/@foe/foe.m
mmse_mrf_demo/+pml/+distributions/@foe/log_grad_x.m
mmse_mrf_demo/+pml/+distributions/@foe/unnorm.m
mmse_mrf_demo/+pml/+distributions/@gsm/
mmse_mrf_demo/+pml/+distributions/@gsm/em.m
mmse_mrf_demo/+pml/+distributions/@gsm/eval.m
mmse_mrf_demo/+pml/+distributions/@gsm/gsm.m
mmse_mrf_demo/+pml/+distributions/@gsm/log_grad_weights.m
mmse_mrf_demo/+pml/+distributions/@gsm/log_grad_x.m
mmse_mrf_demo/+pml/+distributions/@gsm/sample.m
mmse_mrf_demo/+pml/+distributions/@gsm/z_distribution.m
mmse_mrf_demo/+pml/+distributions/@gsm_foe/
mmse_mrf_demo/+pml/+distributions/@gsm_foe/cd.m
mmse_mrf_demo/+pml/+distributions/@gsm_foe/energy_grad_J_tilde.m
mmse_mrf_demo/+pml/+distributions/@gsm_foe/energy_grad_weights.m
mmse_mrf_demo/+pml/+distributions/@gsm_foe/gsm_foe.m
mmse_mrf_demo/+pml/+distributions/@gsm_foe/log_grad_theta.m
mmse_mrf_demo/+pml/+distributions/@gsm_foe/private/
mmse_mrf_demo/+pml/+distributions/@gsm_foe/private/estimator_helper.m
mmse_mrf_demo/+pml/+distributions/@gsm_foe/sample_x.m
mmse_mrf_demo/+pml/+distributions/@gsm_foe/sample_z.m
mmse_mrf_demo/+pml/+distributions/@gsm_foe/z_distribution.m
mmse_mrf_demo/+pml/+distributions/@gsm_pairwise_mrf/
mmse_mrf_demo/+pml/+distributions/@gsm_pairwise_mrf/cd.m
mmse_mrf_demo/+pml/+distributions/@gsm_pairwise_mrf/fit_precision.m
mmse_mrf_demo/+pml/+distributions/@gsm_pairwise_mrf/gsm_pairwise_mrf.m
mmse_mrf_demo/+pml/+distributions/@gsm_pairwise_mrf/log_grad_log_weights.m
mmse_mrf_demo/+pml/+distributions/@gsm_pairwise_mrf/private/
mmse_mrf_demo/+pml/+distributions/@gsm_pairwise_mrf/private/estimator_helper.m
mmse_mrf_demo/+pml/+distributions/@pairwise_mrf/
mmse_mrf_demo/+pml/+distributions/@pairwise_mrf/pairwise_mrf.m
mmse_mrf_demo/+pml/+distributions/density.m
mmse_mrf_demo/+pml/+distributions/distribution.m
mmse_mrf_demo/+pml/+image_proc/
mmse_mrf_demo/+pml/+image_proc/convmtxn.m
mmse_mrf_demo/+pml/+image_proc/imfiltermtx.m
mmse_mrf_demo/+pml/+image_proc/make_convn_mat.m
mmse_mrf_demo/+pml/+image_proc/make_imfilter_mat.m
mmse_mrf_demo/+pml/+image_proc/psnr.m
mmse_mrf_demo/+pml/+image_proc/ssim_index.m
mmse_mrf_demo/+pml/+numerical/
mmse_mrf_demo/+pml/+numerical/checkgrad.m
mmse_mrf_demo/+pml/+numerical/minimize.m
mmse_mrf_demo/+pml/+numerical/sgd.m
mmse_mrf_demo/+pml/+numerical/sle_spd_solver.m
mmse_mrf_demo/+pml/+support/
mmse_mrf_demo/+pml/+support/assemble_images.m
mmse_mrf_demo/+pml/+support/epsr.m
mmse_mrf_demo/+pml/+support/mirror_boundary.m
mmse_mrf_demo/+pml/+support/nefdims.m
mmse_mrf_demo/+pml/+support/randi.m
mmse_mrf_demo/+pml/+support/random_patches.m
mmse_mrf_demo/CHANGELOG
mmse_mrf_demo/Contents.m
mmse_mrf_demo/evaluation/
mmse_mrf_demo/evaluation/demo_evaluation.m
mmse_mrf_demo/evaluation/gen_samples.m
mmse_mrf_demo/evaluation/marginal_stats.m
mmse_mrf_demo/learning/
mmse_mrf_demo/learning/demo_learning.m
mmse_mrf_demo/LICENSE
mmse_mrf_demo/README
mmse_mrf_demo/restoration/
mmse_mrf_demo/restoration/demo_denoising.m
mmse_mrf_demo/restoration/demo_inpainting.m
mmse_mrf_demo/restoration/denoise_mmse.m
mmse_mrf_demo/restoration/images/
mmse_mrf_demo/restoration/images/denoising/
mmse_mrf_demo/restoration/images/denoising/barbara.png
mmse_mrf_demo/restoration/images/denoising/boat.png
mmse_mrf_demo/restoration/images/denoising/fingerprint.png
mmse_mrf_demo/restoration/images/denoising/house.png
mmse_mrf_demo/restoration/images/denoising/lena.png
mmse_mrf_demo/restoration/images/denoising/peppers256.png
mmse_mrf_demo/restoration/images/denoising/README
mmse_mrf_demo/restoration/images/inpainting/
mmse_mrf_demo/restoration/images/inpainting/3ch_mask.png
mmse_mrf_demo/restoration/images/inpainting/3ch_original.png
mmse_mrf_demo/restoration/images/inpainting/new_mask.png
mmse_mrf_demo/restoration/images/inpainting/new_original.png
mmse_mrf_demo/restoration/images/inpainting/README
mmse_mrf_demo/restoration/inpaint_mmse.m

相关说明

  • 本站资源为会员上传分享交流与学习,如有侵犯您的权益,请联系我们删除.
  • 搜珍网是交换下载平台，只提供交流渠道，下载内容来自于网络，除下载问题外，其它问题请自行百度。更多...
  • 本站已设置防盗链，请勿用迅雷、QQ旋风等下载软件下载资源，下载后用WinRAR最新版进行解压.
  • 如果您发现内容无法下载，请稍后再次尝试；或换浏览器；或者到消费记录里找到下载记录反馈给我们.
  • 下载后发现下载的内容跟说明不相乎，请到消费记录里找到下载记录反馈给我们，经确认后退回积分.
  • 如下载前有疑问，可以通过点击"提供者"的名字，查看对方的联系方式，联系对方咨询.
查看所有评论

相关评论

暂无评论内容．

发表评论

*快速评论： 推荐 一般 有密码 和说明不符 不是源码或资料 文件不全 不能解压 纯粹是垃圾
*内　　容：
*验 证 码：
搜珍网 www.dssz.com