Good Regions to Deblur 

The goal of single image deblurring is to recover both a latent clear image and an underlying blur kernel from one input blurred image. Recent works focus on exploiting natural image priors or additional image observations for deblurring, but pay less attention to the influence of image structures on estimating blur kernels. What is the useful image structure and how can one select good regions for deblurring? We formulate the problem of learning good regions for deblurring within the Conditional Random Field framework. To better compare blur kernels, we develop an effective similarity metric for labelling training samples. The learned model is able to predict good regions from an input blurred image for deblurring without user guidance. Qualitative and quantitative evaluations demonstrate that good regions can be selected by the proposed algorithms for effective image deblurring. When time permits, I will also present some recent results on fast non-uniform image deblurring.
Ming-Hsuan Yang is an assistant professor in Electrical Engineering and Computer Science at University of California, Merced. He received the PhD degree in computer science from the University of Illinois at Urbana-Champaign in 2000. He has served as an area chair for several conferences including IEEE Conference on Computer Vision and Pattern Recognition (CVPR) in 2008 and 2009; IEEE International Conference on Computer Vision (ICCV) in 2011; Asian Conference on Computer (ACCV) in 2009, 2010, and 2012; AAAI National Conference on Artificial Intelligence (AAAI) in 2011; and IEEE International Conference on Automatic Face and Gesture Recognition (FG) in 2011 and 2013. He served as an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) from 2007 to 2011, and currently as an associate editor of the Image and Vision Computing (IVC). Yang received the Google Faculty Award in 2009, and the Distinguished Early Career Research award from the UC Merced senate in 2011. Yang is a recipient of the Faculty Early Career Development (CAREER) award from the National Science Foundation in 2012. He is a senior member of the IEEE and the ACM.