Blind image deblurring based on dictionary replacing
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Blind correction of optical aberrations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Blur-Kernel estimation from spectral irregularities
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Bayesian blind deconvolution with general sparse image priors
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
A theoretical analysis of camera response functions in image deblurring
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Iris image deblurring based on refinement of point spread function
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Patch mosaic for fast motion deblurring
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Robust image deblurring using hyper laplacian model
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Disparity-based space-variant image deblurring
Image Communication
A no-reference metric for evaluating the quality of motion deblurring
ACM Transactions on Graphics (TOG)
Kernel estimation from salient structure for robust motion deblurring
Image Communication
Robust blind motion deblurring using near-infrared flash image
Journal of Visual Communication and Image Representation
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Blind image deconvolution is an ill-posed problem that requires regularization to solve. However, many common forms of image prior used in this setting have a major drawback in that the minimum of the resulting cost function does not correspond to the true sharp solution. Accordingly, a range of additional methods are needed to yield good results (Bayesian methods, adaptive cost functions, alpha-matte extraction and edge localization). In this paper we introduce a new type of image regularization which gives lowest cost for the true sharp image. This allows a very simple cost formulation to be used for the blind deconvolution model, obviating the need for additional methods. Due to its simplicity the algorithm is fast and very robust. We demonstrate our method on real images with both spatially invariant and spatially varying blur.