Fundamentals of digital image processing
Fundamentals of digital image processing
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Iterative methods for total variation denoising
SIAM Journal on Scientific Computing - Special issue on iterative methods in numerical linear algebra; selected papers from the Colorado conference
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization
Journal of Mathematical Imaging and Vision
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
SSIAI '08 Proceedings of the 2008 IEEE Southwest Symposium on Image Analysis and Interpretation
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Total variation minimization and a class of binary MRF models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Iterative learning algorithms for linear Gaussian observation models
IEEE Transactions on Signal Processing
Compound Gauss-Markov random fields for image estimation
IEEE Transactions on Signal Processing
ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems
IEEE Transactions on Signal Processing
Total variation blind deconvolution
IEEE Transactions on Image Processing
Bayesian and regularization methods for hyperparameter estimation in image restoration
IEEE Transactions on Image Processing
An EM algorithm for wavelet-based image restoration
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Majorization–Minimization Algorithms for Wavelet-Based Image Restoration
IEEE Transactions on Image Processing
On some Bayesian/regularization methods for image restoration
IEEE Transactions on Image Processing
Nonlinear regularization techniques for seismic tomography
Journal of Computational Physics
A weberized total variation regularization-based image multiplicative noise removal algorithm
EURASIP Journal on Advances in Signal Processing
Fast communication: Some empirical advances in matrix completion
Signal Processing
An MLP neural net with L1 and L2 regularizers for real conditions of deblurring
EURASIP Journal on Advances in Signal Processing
Total variation blind deconvolution employing split Bregman iteration
Journal of Visual Communication and Image Representation
Variational structure-texture image decomposition on manifolds
Signal Processing
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This paper presents a new approach to image deconvolution (deblurring), under total variation (TV) regularization, which is adaptive in the sense that it does not require the user to specify the value of the regularization parameter. We follow the Bayesian approach of integrating out this parameter, which is achieved by using an approximation of the partition function of the Bayesian prior interpretation of the TV regularizer. The resulting optimization problem is then attacked using a majorization-minimization algorithm. Although the resulting algorithm is of the iteratively reweighted least squares (IRLS) type, thus suffering of the infamous ''singularity issue'', we show that this issue is in fact not problematic, as long as adequate initialization is used. Finally, we report experimental results showing that the proposed methodology achieves state-of-the-art performance, on par with TV-based methods with hand tuned regularization parameters, as well as with the best wavelet-based methods.