文件名称:mmse_mrf_demo-1.1
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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
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
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下载文件列表
mmse_mrf_demo
.............\+image_patches
.............\..............\load.m
.............\..............\training
.............\..............\validation
.............\+learned_models
.............\...............\cvpr_3x3_foe.m
.............\...............\cvpr_pw_mrf.m
.............\+pml
.............\....\+distributions
.............\....\..............\@discrete
.............\....\..............\.........\discrete.m
.............\....\..............\.........\eval.m
.............\....\..............\.........\kl_divergence.m
.............\....\..............\.........\mle.m
.............\....\..............\.........\plot.m
.............\....\..............\.........\private
.............\....\..............\.........\.......\montecarlo.m
.............\....\..............\.........\sample.m
.............\....\..............\.........\semilogy.m
.............\....\..............\.........\test
.............\....\..............\.........\....\test_all.m
.............\....\..............\@foe
.............\....\..............\....\energy.m
.............\....\..............\....\eval.m
.............\....\..............\....\foe.m
.............\....\..............\....\log_grad_x.m
.............\....\..............\....\unnorm.m
.............\....\..............\@gsm
.............\....\..............\....\em.m
.............\....\..............\....\eval.m
.............\....\..............\....\gsm.m
.............\....\..............\....\log_grad_weights.m
.............\....\..............\....\log_grad_x.m
.............\....\..............\....\sample.m
.............\....\..............\....\z_distribution.m
.............\....\..............\@gsm_foe
.............\....\..............\........\cd.m
.............\....\..............\........\energy_grad_J_tilde.m
.............\....\..............\........\energy_grad_weights.m
.............\....\..............\........\gsm_foe.m
.............\....\..............\........\log_grad_theta.m
.............\....\..............\........\private
.............\....\..............\........\.......\estimator_helper.m
.............\....\..............\........\sample_x.m
.............\....\..............\........\sample_z.m
.............\....\..............\........\z_distribution.m
.............\....\..............\@gsm_pairwise_mrf
.............\....\..............\.................\cd.m
.............\....\..............\.................\fit_precision.m
.............\....\..............\.................\gsm_pairwise_mrf.m
.............\....\..............\.................\log_grad_log_weights.m
.............\....\..............\.................\private
.............\....\..............\.................\.......\estimator_helper.m
.............\....\..............\@pairwise_mrf
.............\....\..............\.............\pairwise_mrf.m
.............\....\..............\density.m
.............\....\..............\distribution.m
.............\....\+image_proc
.............\....\...........\convmtxn.m
.............\....\...........\imfiltermtx.m
.............\....\...........\make_convn_mat.m
.............\....\...........\make_imfilter_mat.m
.............\....\...........\psnr.m
.............\....\...........\ssim_index.m
.............\....\+numerical
.............\....\..........\checkgrad.m
.............\....\..........\minimize.m
.............\....\..........\sgd.m
.............\....\..........\sle_spd_solver.m
.............\....\+support
.............\....\........\assemble_images.m
.............\....\........\epsr.m
.............\....\........\mirror_boundary.m
.............\....\........\nefdims.m
.............\....\........\randi.m
.............\....\........\random_patches.m
.............\CHANGELOG
.............\Contents.m
.............\evaluation
.............\..........\demo_evaluation.m
.............\..........\gen_samples.m
.............\..........\marginal_stats.m
.............\learning
.............\........\demo_learning.m
.............\LICENSE
.............\README
.............\restoration
.............\...........\demo_denoising.m
.............\........