Fundamentals of digital image processing
Fundamentals of digital image processing
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Progressive inter-scale and intra-scale non-blind image deconvolution
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH Asia 2009 papers
A fast multilevel algorithm for wavelet-regularized image restoration
IEEE Transactions on Image Processing
Efficient marginal likelihood optimization in blind deconvolution
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A novel blind deconvolution scheme for image restoration usingrecursive filtering
IEEE Transactions on Signal Processing
A regularization approach to joint blur identification and image restoration
IEEE Transactions on Image Processing
Blur identification by the method of generalized cross-validation
IEEE Transactions on Image Processing
Majorization–Minimization Algorithms for Wavelet-Based Image Restoration
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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Blind deconvolution aims at reconstructing an image from its blurred and noisy version, when the blur kernel is not known. It has been acknowledged that the naive maximum aposteriori probability (MAP) algorithm favors a no-blur solution [3]. In [8] the failure of the direct MAP approach is addressed and it is proved that a simultaneous MAP estimation of the image and the point spread function (PSF) fails, providing a trivial solution. In contrast, we show that an appropriate choice of PSF prior during joint MAP estimation does provide a non-trivial solution. We provide the feasible range for the PSF regularization factor which would prevent a trivial solution.