ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A New Multichannel Blind Deconvolution Method and Its Application to Solar Images
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Enhanced Biggs---Andrews Asymmetric Iterative Blind Deconvolution
Multidimensional Systems and Signal Processing
Spatially adaptive intensity bounds for image restoration
EURASIP Journal on Applied Signal Processing
Efficient recursive multichannel blind image restoration
EURASIP Journal on Applied Signal Processing
Iterative desensitisation of image restoration filters under wrong PSF and noise estimates
EURASIP Journal on Applied Signal Processing
Nonlinear image restoration using a radial basis function network
EURASIP Journal on Applied Signal Processing
Automatic matching of high-resolution SAR images
International Journal of Remote Sensing
Variational Deconvolution of Multi-channel Images with Inequality Constraints
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Sequential Blind PSF Estimation and Restoration of Aerial Multispectral Images
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Geometric Approach to Measure-Based Metric in Image Segmentation
Journal of Mathematical Imaging and Vision
Journal of Artificial Intelligence Research
Variational Bayesian blind deconvolution using a total variation prior
IEEE Transactions on Image Processing
Out-of-focus Blur estimation for blind image deconvolution: using particle swarm optimization
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Edge enhancement by local deconvolution
Pattern Recognition
A new and general method for blind shift-variant deconvolution of biomedical images
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Iterative PSF estimation and its application to shift invariant and variant blur reduction
EURASIP Journal on Advances in Signal Processing
Image enhancement method VIA blur and noisy image fusion
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Blind and semi-blind deblurring of natural images
IEEE Transactions on Image Processing
Low-light imaging solutions for mobile devices
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
An iterative method for preserving edges and reducing noise in high resolution image reconstruction
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Double regularized bayesian estimation for blur identification in video sequences
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Exploiting DLP Illumination Dithering for Reconstruction and Photography of High-Speed Scenes
International Journal of Computer Vision
Total variation blind deconvolution employing split Bregman iteration
Journal of Visual Communication and Image Representation
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
Blur-Kernel estimation from spectral irregularities
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Joint MAP estimation for blind deconvolution: when does it work?
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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The primary difficulty with blind image restoration, or joint blur identification and image restoration, is insufficient information. This calls for proper incorporation of a priori knowledge about the image and the point-spread function (PSF). A well-known space-adaptive regularization method for image restoration is extended to address this problem. This new method effectively utilizes, among others, the piecewise smoothness of both the image and the PSF. It attempts to minimize a cost function consisting of a restoration error measure and two regularization terms (one for the image and the other for the blur) subject to other hard constraints. A scale problem inherent to the cost function is identified, which, if not properly treated, may hinder the minimization/blind restoration process. Alternating minimization is proposed to solve this problem so that algorithmic efficiency as well as simplicity is significantly increased. Two implementations of alternating minimization based on steepest descent and conjugate gradient methods are presented. Good performance is observed with numerically and photographically blurred images, even though no stringent assumptions about the structure of the underlying blur operator is made