文件名称:deblurring_demo-1.0
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Bayesian Deblurring with Integrated Noise Estimation-Bayesian Deblurring with Integrated Noise Estimation
Conventional non-blind image deblurring algorithms
involve natural image priors and maximum a-posteriori
(MAP) estimation. As a consequence of MAP estimation,
separate pre-processing steps such as noise estimation and
training of the regularization parameter are necessary to
avoid user interaction. Moreover, MAP estimates involving
standard natural image priors have been found lacking in
terms of restoration performance. To address these issues
we introduce an integrated Bayesian fr a mework that unifies
non-blind deblurring and noise estimation, thus freeing the
user of tediously pre-determining a noise level. A samplingbased
technique allows to integrate out the unknown noise
level and to perform deblurring using the Bayesian minimum
mean squared error estimate (MMSE), which requires
no regularization parameter and yields higher performance
than MAP estimates when combined with a learned highorder
image prior. A quan
Conventional non-blind image deblurring algorithms
involve natural image priors and maximum a-posteriori
(MAP) estimation. As a consequence of MAP estimation,
separate pre-processing steps such as noise estimation and
training of the regularization parameter are necessary to
avoid user interaction. Moreover, MAP estimates involving
standard natural image priors have been found lacking in
terms of restoration performance. To address these issues
we introduce an integrated Bayesian fr a mework that unifies
non-blind deblurring and noise estimation, thus freeing the
user of tediously pre-determining a noise level. A samplingbased
technique allows to integrate out the unknown noise
level and to perform deblurring using the Bayesian minimum
mean squared error estimate (MMSE), which requires
no regularization parameter and yields higher performance
than MAP estimates when combined with a learned highorder
image prior. A quan
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下载文件列表
deblurring_demo
...............\+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
...............\....\..............\....\test
...............\....\..............\....\....\test_all.m
...............\....\..............\....\....\test_density.m
...............\....\..............\....\....\test_ho_derivatives.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
...............\....\..............\........\test
...............\....\..............\........\....\test_density.m
...............\....\..............\........\....\test_filter.m
...............\....\..............\........\....\test_learning.m
...............\....\..............\........\....\test_sampling.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
...............\....\..............\.................\test
...............\....\..............\.................\....\test_density.m
...............\....\..............\.................\....\test_learning.m
...............\....\..............\.................\....\test_statistics.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
...............\....\....