TY - GEN
T1 - Progressive blind deconvolution
AU - Hanocka, Rana
AU - Kiryati, Nahum
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - We present a novel progressive framework for blind image restoration. Common blind restoration schemes first estimate the blur kernel, then employ non-blind deblurring. However, despite recent progress, the accuracy of PSF estimation is limited. Furthermore, the outcome of non-blind deblurring is highly sensitive to errors in the assumed PSF. Therefore, high quality blind deblurring has remained a major challenge. In this work, we combine state of the art regularizers for the image and the PSF, namely the Mumford & Shah piecewisesmooth image model and the sparse PSF prior. Previous works that used Mumford & Shah image regularization were either limited to nonblind deblurring or semi-blind deblurring assuming a parametric kernel known up to an unknown parameter. We suggest an iterative progressive restoration scheme, in which the imperfectly deblurred output of the current iteration is fed back as input to the next iteration. The kernel representing the residual blur is then estimated, and used to drive the non-blind restoration component, leading to finer deblurring. Experimental results demonstrate rapid convergence, and excellent performance on a wide variety of blurred images.
AB - We present a novel progressive framework for blind image restoration. Common blind restoration schemes first estimate the blur kernel, then employ non-blind deblurring. However, despite recent progress, the accuracy of PSF estimation is limited. Furthermore, the outcome of non-blind deblurring is highly sensitive to errors in the assumed PSF. Therefore, high quality blind deblurring has remained a major challenge. In this work, we combine state of the art regularizers for the image and the PSF, namely the Mumford & Shah piecewisesmooth image model and the sparse PSF prior. Previous works that used Mumford & Shah image regularization were either limited to nonblind deblurring or semi-blind deblurring assuming a parametric kernel known up to an unknown parameter. We suggest an iterative progressive restoration scheme, in which the imperfectly deblurred output of the current iteration is fed back as input to the next iteration. The kernel representing the residual blur is then estimated, and used to drive the non-blind restoration component, leading to finer deblurring. Experimental results demonstrate rapid convergence, and excellent performance on a wide variety of blurred images.
KW - Image deblurring
KW - Mumford & Shah regularization
KW - Piecewise-smooth image model
KW - Progressive blind restoration
KW - Residual blur removal
KW - Sparse PSF prior
UR - http://www.scopus.com/inward/record.url?scp=84945923619&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23117-4_27
DO - 10.1007/978-3-319-23117-4_27
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:84945923619
SN - 9783319231167
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 313
EP - 325
BT - Computer Analysis of Images and Patterns - 16th International Conference, CAIP 2015, Proceedings
A2 - Azzopardi, George
A2 - Petkov, Nicolai
PB - Springer Verlag
T2 - 16th International Conference on Computer Analysis of Images and Patterns, CAIP 2015
Y2 - 2 September 2015 through 4 September 2015
ER -