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
Computer Vision
Digital Image Processing in Remote Sensing
Digital Image Processing in Remote Sensing
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Image Deblurring in the Presence of Impulsive Noise
International Journal of Computer Vision
Image deblurring with blurred/noisy image pairs
ACM SIGGRAPH 2007 papers
Variational Bayesian blind deconvolution using a total variation prior
IEEE Transactions on Image Processing
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
Blind deblurring of foreground-background images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Variational deblurring of images with uncertain and spatially variant blurs
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
A recursive soft-decision approach to blind image deconvolution
IEEE Transactions on Signal Processing
A regularization approach to joint blur identification and image restoration
IEEE Transactions on Image Processing
Total variation blind deconvolution
IEEE Transactions on Image Processing
Blind image restoration by anisotropic regularization
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Blind deconvolution of images using optimal sparse representations
IEEE Transactions on Image Processing
Multichannel blind deconvolution of spatially misaligned images
IEEE Transactions on Image Processing
A spatially adaptive nonparametric regression image deblurring
IEEE Transactions on Image Processing
Reconstructing arbitrarily focused images from two differently focused images using linear filters
IEEE Transactions on Image Processing
Bayesian Restoration Using a New Nonstationary Edge-Preserving Image Prior
IEEE Transactions on Image Processing
Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation
IEEE Transactions on Image Processing
Space-Variant Restoration of Images Degraded by Camera Motion Blur
IEEE Transactions on Image Processing
Parameter Estimation in TV Image Restoration Using Variational Distribution Approximation
IEEE Transactions on Image Processing
Variational Bayesian Image Restoration Based on a Product of -Distributions Image Prior
IEEE Transactions on Image Processing
Blind Image Deconvolution Through Support Vector Regression
IEEE Transactions on Neural Networks
Blur Identification by Multilayer Neural Network Based on Multivalued Neurons
IEEE Transactions on Neural Networks
Blind deblurring of foreground-background images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Blind image deblurring based on dictionary replacing
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Geophysical model enhancement technique based on blind deconvolution
Computers & Geosciences
No-reference blur image quality measure based on multiplicative multiresolution decomposition
Journal of Visual Communication and Image Representation
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A method for blind image deblurring is presented. The method only makes weak assumptions about the blurring filter and is able to undo a wide variety of blurring degradations. To overcome the ill-posedness of the blind image deblurring problem, the method includes a learning technique which initially focuses on the main edges of the image and gradually takes details into account. A new image prior, which includes a new edge detector, is used. The method is able to handle unconstrained blurs, but also allows the use of constraints or of prior information on the blurring filter, as well as the use of filters defined in a parametric manner. Furthermore, it works in both single-frame and multiframe scenarios. The use of constrained blur models appropriate to the problem at hand, and/or of multiframe scenarios, generally improves the deblurring results. Tests performed on monochrome and color images, with various synthetic and real-life degradations, without and with noise, in single-frame and multiframe scenarios, showed good results, both in subjective terms and in terms of the increase of signal to noise ratio (ISNR) measure. In comparisons with other state of the art methods, our method yields better results, and shows to be applicable to a much wider range of blurs.