Description: 小波域的树模型去噪采用ＥＭ算法训练模型参数由Ｍatlab编程实现仿真-Wavelet denoising tree model using EM algorithm training model parameters from Matlab Simulation Programming Platform: |
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Author:逻辑 |
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Description: 基于变分贝叶斯估计的相机抖动模糊图像的盲复原算法.pdf-Blurred image of the blind restoration algorithm based on variational Bayesian estimation of camera jitter. Pdf Platform: |
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Author:li |
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Description: 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 Platform: |
Size: 1216512 |
Author:孙文义 |
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Description: 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 framework 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 Platform: |
Size: 904192 |
Author:孙文义 |
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Search - bayesian image restoration