Proceedings Article10.1109/ICASSP.2006.1660477
Blind Image Restoration Using a Block-Stationary Signal Model
Tom E. Bishop,James R. Hopgood +1 more
- 14 May 2006
- Vol. 2, pp 853-856
1.1K
TL;DR: A novel method for blind image restoration which is a multidimensional extension of an approach used successfully for audio restoration, and a maximum marginalised a posteriori (MMAP) blur estimate is obtained by optimising the resulting probability density function.
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Abstract: We present a novel method for Blind image restoration which is a multidimensional extension of an approach used successfully for audio restoration. A nonstationary image model is used to increase reliability of blur estimates. This source model consists of a separate autoregressive model in each region of the image. A hierarchical Bayesian model for the observations is used, and a maximum marginalised a posteriori (MMAP) blur estimate is obtained by optimising the resulting probability density function.
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References
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Fundamentals of digital image processing
Anil K. Jain
- 03 Oct 1988
TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
9K
Blind image deconvolution
TL;DR: The problem of blind deconvolution for images is introduced, the basic principles and methodologies behind the existing algorithms are provided, and the current trends and the potential of this difficult signal processing problem are examined.
1.4K
Blind image restoration by anisotropic regularization
Yu-Li You,Mostafa Kaveh +1 more
TL;DR: Anisotropic regularization techniques are presented to exploit the piecewise smoothness of the image and the point spread function (PSF) in order to mitigate the severe lack of information encountered in blind restoration of shift-invariantly and shift-variantly blurred images.
171
Blind single channel deconvolution using nonstationary signal processing
TL;DR: The proposed Bayesian method does take account for the channel's stationarity in the model and is more robust, and the advantage of utilizing the nonstationarity of a system rather than considering it as a curse is discussed.
A soft double regularization approach to parametric blind image deconvolution
Li Chen,Kim-Hui Yap +1 more
TL;DR: A blind image deconvolution scheme based on soft integration of parametric blur structures, and incorporating the knowledge into the parametric double regularization (PDR) scheme, which is effective in restoring degraded images under different environments.
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