An adaptively accelerated Lucy-Richardson method for image deblurring
EURASIP Journal on Advances in Signal Processing
A nonparametric procedure for blind image deblurring
Computational Statistics & Data Analysis
Sparse coding via thresholding and local competition in neural circuits
Neural Computation
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
Identification of Piecewise Linear Uniform Motion Blur
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Multiple-Image-Based Restoration for Motion Blur with Non-uniform Point Spread Function
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A Predual Proximal Point Algorithm Solving a Non Negative Basis Pursuit Denoising Model
International Journal of Computer Vision
A fast approach for overcomplete sparse decomposition based on smoothed l0 norm
IEEE Transactions on Signal Processing
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
Generalizing the Nonlocal-means to super-resolution reconstruction
IEEE Transactions on Image Processing
A proximal iteration for deconvolving Poisson noisy images using sparse representations
IEEE Transactions on Image Processing
Sparse image reconstruction for molecular imaging
IEEE Transactions on Image Processing
A fast multilevel algorithm for wavelet-regularized image restoration
IEEE Transactions on Image Processing
Wavelet-regularized reconstruction for rapid MRI
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
L1 prior majorization in Bayesian image restoration
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Comparison of SPARLS and RLS algorithms for adaptive filtering
SARNOFF'09 Proceedings of the 32nd international conference on Sarnoff symposium
IEEE Transactions on Image Processing
An iterative Bayesian algorithm for sparse component analysis in presence of noise
IEEE Transactions on Signal Processing
A SURE approach for digital signal/image deconvolution problems
IEEE Transactions on Signal Processing
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
A subband adaptive iterative shrinkage/thresholding algorithm
IEEE Transactions on Signal Processing
CIDER: corrected inverse-denoising filter for image restoration
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Coherence analysis of iterative thresholding algorithms
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Image deconvolution by stein block thresholding
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An overview of inverse problem regularization using sparsity
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Monotone operator splitting for optimization problems in sparse recovery
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A hyperspectral image restoration technique
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Image restoration by mixture modelling of an overcomplete linear representation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Model-based compressive sensing
IEEE Transactions on Information Theory
Information Sciences: an International Journal
SPARLS: the sparse RLS algorithm
IEEE Transactions on Signal Processing
Fast image recovery using variable splitting and constrained optimization
IEEE Transactions on Image Processing
Block Based Deconvolution Algorithm Using Spline Wavelet Packets
Journal of Mathematical Imaging and Vision
On the stable recovery of the sparsest overcomplete representations in presence of noise
IEEE Transactions on Signal Processing
Restoration of Poissonian images using alternating direction optimization
IEEE Transactions on Image Processing
Journal of Mathematical Imaging and Vision
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
Exact optimization for the l1-Compressive Sensing problem using a modified Dantzig-Wolfe method
Theoretical Computer Science
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
SIAM Journal on Scientific Computing
Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
SIAM Journal on Imaging Sciences
Learning the Morphological Diversity
SIAM Journal on Imaging Sciences
A coordinate gradient descent method for l1-regularized convex minimization
Computational Optimization and Applications
Adaptive algorithms for sparse system identification
Signal Processing
Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation
The Journal of Machine Learning Research
Image sharpening by DWT-based hysteresis
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Pixel-level image fusion with simultaneous orthogonal matching pursuit
Information Fusion
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Journal of Visual Communication and Image Representation
Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
SIAM Journal on Scientific Computing
Gradient-Based Methods for Sparse Recovery
SIAM Journal on Imaging Sciences
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences
Alternating Direction Method for Image Inpainting in Wavelet Domains
SIAM Journal on Imaging Sciences
Image denoising using the lyapunov equation from non-uniform samples
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
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Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
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Physical Communication
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Journal of Visual Communication and Image Representation
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International Journal of Imaging Systems and Technology
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ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Computers & Geosciences
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Journal of Scientific Computing
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Journal of Signal Processing Systems
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Knowledge-Based Systems
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated wavelet-based restoration but, except for certain special cases, the resulting criteria are solved approximately or require demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. Thus, it is a general-purpose approach to wavelet-based image restoration with computational complexity comparable to that of standard wavelet denoising schemes or of frequency domain deconvolution methods. The algorithm alternates between an E-step based on the fast Fourier transform (FFT) and a DWT-based M-step, resulting in an efficient iterative process requiring O(NlogN) operations per iteration. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach performs competitively with, in some cases better than, the best existing methods in benchmark tests.