Recent Advances in Image and Video Deblurring A minisymposium, part of:
	Summary: Deblurring is one of the most challenging inverse problems in imaging. Over the years, a diverse array of techniques have been applied to this problem in different application areas, with mixed success. In only the last 5 years, a sudden and significant improvement of performance has been realized, particularly in the case of blind deblurring. A flurry of related activity has followed in the fields of computer vision, graphics, and image processing. This workshop gathered leading researchers in this area to discuss recent progress and state of the art in image and video deblurring. Organizer: Peyman Milanfar (University of California, Santa Cruz)
	A Variational Approach to Blind Image Deconvolution Rob Fergus, Computer Science Department, New York University    Deblurring with Advanced Optimization Qi Shan,Univ. of Washington, Leo Jia, Chinese Univ. of Hong Kong, and Aseem Agarwala, Adobe   Superresolution and blind deconvolution of video Filip Sroubek, and Jan Flusser, Academy of Sciences, Czech Republic   Variational Formulation for Nonlocal Image Deblurring with Collaborative L_0-Norm Prior Vladimir Katkovnik, and Karen Egiazarian, Alessandro Foi, Tampere Univ. of Technology, Finland   Optimizing the Blur-Noise Tradeoff with Multiple-Photo Capture Sam Hasinoff, MIT; Kiriakos Kutulakos, Univ. of Toronto; Fredo Durand and William Freeman, MIT
back to top	Last Updated April 26, 2010