An adaptively accelerated Lucy-Richardson method for image deblurring
EURASIP Journal on Advances in Signal Processing
Blind Estimation of Motion Blur Parameters for Image Deconvolution
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
A non-local regularization strategy for image deconvolution
Pattern Recognition Letters
A Predual Proximal Point Algorithm Solving a Non Negative Basis Pursuit Denoising Model
International Journal of Computer Vision
Generalizing the Nonlocal-means to super-resolution reconstruction
IEEE Transactions on Image Processing
A fast multilevel algorithm for wavelet-regularized image restoration
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Counter-examples for Bayesian MAP restoration
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
IEEE Transactions on Image Processing
A weberized total variation regularization-based image multiplicative noise removal algorithm
EURASIP Journal on Advances in Signal Processing
Effective image restorations using a novel spatial adaptive prior
EURASIP Journal on Advances in Signal Processing
Learning the Morphological Diversity
SIAM Journal on Imaging Sciences
Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation
The Journal of Machine Learning Research
Deconvolving Poissonian images by a novel hybrid variational model
Journal of Visual Communication and Image Representation
Multicomponent image restoration, an experimental study
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Joint source and sending rate modeling in adaptive video streaming
Image Communication
A boundary condition based deconvolution framework for image deblurring
Journal of Computational and Applied Mathematics
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Image deconvolution is formulated in the wavelet domain under the Bayesian framework. The well-known sparsity of the wavelet coefficients of real-world images is modeled by heavy-tailed priors belonging to the Gaussian scale mixture (GSM) class; i.e., priors given by a linear (finite of infinite) combination of Gaussian densities. This class includes, among others, the generalized Gaussian, the Jeffreys , and the Gaussian mixture priors. Necessary and sufficient conditions are stated under which the prior induced by a thresholding/shrinking denoising rule is a GSM. This result is then used to show that the prior induced by the "nonnegative garrote" thresholding/shrinking rule, herein termed the garrote prior, is a GSM. To compute the maximum a posteriori estimate, we propose a new generalized expectation maximization (GEM) algorithm, where the missing variables are the scale factors of the GSM densities. The maximization step of the underlying expectation maximization algorithm is replaced with a linear stationary second-order iterative method. The result is a GEM algorithm of O(NlogN) computational complexity. In a series of benchmark tests, the proposed approach outperforms or performs similarly to state-of-the art methods, demanding comparable (in some cases, much less) computational complexity.