Multi-scale blind motion deblurring using local minimum

Inverse Problems

Volumn 26, Issue 1, 2010, Pages

  Wang, Chao ^a   Sun, Lifeng ^a   Chen, Zhuoyuan ^a   Zhang, Jianwei ^b   Yang, Shiqiang ^a  

^a TSINGHUA UNIVERSITY   (China)

^b UNIVERSITY OF HAMBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BLIND DECONVOLUTION; BLURRY IMAGES; COARSE TO FINE; DATA SETS; GLOBAL MINIMA; LOCAL MINIMUMS; MAP APPROACH; MOTION DEBLURRING; MULTISCALES;

INVERSE PROBLEMS; STATISTICAL TESTS;

CONVOLUTION;

EID: 74549163652     PISSN: 02665611     EISSN: 13616420     Source Type: Journal    
DOI: 10.1088/0266-5611/26/1/015003     Document Type: Article

References (35)

1. Yuan L, Sun J, Quan L and Shum H Y 2008 Progressive inter-scale and intra-scale non-blind image deconvolution ACM Trans. Graph. 27 74
2. Levin A, Fergus R, Durand F and Freeman B 2007 Image and depth from a conventional camera with a coded aperture SIGGRAPH
3. Neelamani R, Choi H and Baraniuk R 2004 Forward: Fourier-wavelet regularized deconvolution for ill-conditioned systems IEEE Trans. Signal Process. 52 418-33
4. Dey N, Fraud L B, Zimmer C, Kam Z, Roux P, Marin J O and Zerubia J 2006 Richardson Lucy algorithm with total variation regularization for 3d confocal microscope deconvolution Microsc. Res. Tech. 69 260-6
5. Donatelli M, Estatico C, Martinelli A and Capizzano S 2006 Improved image deblurring with antireflective boundary conditions and re-blurring Inverse Problems 22 2035-53
6. Donatelli M and Capizzano S 2005 Anti-reflective boundary conditions and re-blurring Inverse Problems 21 169-82
7. Fergus R, Singh B, Hertzmann A, Roweis S T and Freeman W T 2006 Removing camera shake from a single photograph ACM Trans. Graph. 25 787-94
8. Jia J Y 2007 Single image motion deblurring using transparency IEEE Conf. on Computer Vision and Pattern Recognition
9. Miskin J and Mackay D J C 2000 Ensemble learning for blind image separation and deconvolution Advances in Independent Component Analysis ed Girolani M (Berlin: Springer)
10. Shan Q, Xiong W and Jia J Y 2007 Rotational motion deblurring of a rigid object from a single image IEEE Int. Conf. on Computer Vision
11. Bronstein M M, Bronstein A M, Zibulevsky M and Zeevi Y Y 2005 Blind deconvolution of images using optimal sparse representations IEEE Trans. Image Process. 14 726-36
12. Joshi N, Szeliski R and Kriegman D 2008 PSF estimation using sharp edge prediction IEEE Conf. on Computer Vision and Pattern Recognition
13. Levin A 2006 Blind motion deblurring using image statistics Advances in Neural Information Processing Systems (NIPS) vol 11 pp 841-8
14. Levin A, Weiss Y, Durand F and Freeman W T 2009 Understanding and evaluating blind deconvolution algorithms IEEE Conf. on Computer Vision and Pattern Recognition
15. Yuan L, Sun J, Quan L and Shum H Y 2007 Image deblurring with blurred/noisy image pairs ACM Trans. Graph. 26 1-10
16. Shan Q, Jia J Y and Agarwala A 2008 High-quality motion deblurring from a single image ACM Trans. Graph. 27 1
17. Kundur D and Hatzinakos D 1996 Blind image deconvolution IEEE Signal Process. Mag. 13 43-64
18. Chan T F and Shen J 2006 Theory and computation of variational image deblurring IMS Lecture Notes
19. Luck L 1974 An iterative technique for rectification of observed distributions J. Astron. 79 745-54
20. Richardson W 1972 Bayesian-based iterative method of image restoration J. Opt. Soc. Am. 62 55-9
21. Caron J, Namazi N and Rollins C 2002 Noniterative blind data restoration by use of an extracted filter function Appl. Opt. 41 68-84
22. Jalobeanu A, Fraud L B and Zerubia J 2002 Estimation of blur and noise parameters in remote sensing Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing pp 249-56
23. Ayers G R, Dainty J C and Zerubia J 1988 Iterative blind deconvolution method and its applications Opt. Lett. 13 547-9
24. Katsaggelos A K and Lay K T 1991 Maximum likelihood blur identification and image restoration using the em algorithm IEEE Trans. Signal Process. 39 729-33
25. Likas A C and Galatsanos N P 2004 A variational approach for bayesian blind image deconvolution IEEE Trans. Signal Process. 52 2222-33
26. Molina R, Katsaggelos A K, Abad J and Mateos J 1997 A bayesian approach to blind deconvolution based on dirichlet distributions Int. Conf. on Acoustics, Speech and Signal Processing
27. Thiébaut E and Conan J M 1995 Strict a priori constraints for maximum-likelihood blind deconvolution J. Opt. Soc. Am. A 12 485-92
28. Cai J F, Ji H, Liu C Q and Shen Z W 2009 Blind motion deblurring from a single image using sparse approximation IEEE Conf. on Computer Vision and Pattern Recognition
29. Roth S and Black M J 2005 Fields of experts: a framework for learning image priors IEEE Conf. on Computer Vision and Pattern Recognition
30. Kim S J, Koh K, Lustig M and Boyd S 2007 An efficient method for compressed sensing The Int. Conf. on Image Processing
31. Liu C, Szeliski R, Kang S B, Zitnick C L and Freeman W T 2008 Automatic estimation and removal of noise from a single image IEEE Trans. Pattern Anal. Mach. Intell. 30 299-314
32. Calvetti D and Somersalo E 2005 Statistical elimination of boundary artefacts in image deblurring Inverse Problems 21 1697-714
33. Liu R and Jia J Y 2008 Reducing boundary artifacts in image deconvolution The Int. Conf. on Image Processing
34. Morigi S, Reichel L, Sgallari F and Shyshkov A 2008 Cascadic multiresolution methods for image deblurring SIAM J. Imaging Sci. 1 51-74
35. Weiss L and Freeman J 2007 What makes a good model of natural images? IEEE Conf. on Computer Vision and Pattern Recognition