Minisymposium on Locally Adaptive Patch-based Image and Video Restoration |
|
Summary: The past few years have witnessed the emergence of a series of papers that tackle various image processing tasks in a locally adaptive, patch-based manner. These methods serve various applications, such as denoising and deblurring of still images and video, inpainting, image decomposition, segmentation, super-resolution reconstruction, compression, and more. As the name suggests, these techniques operate locally in the image, applying the same process for every pixel by manipulating small patches of pixels. As such, these methods are typically simple, highly parallelizable. Remarkably, in most cases, the proposed algorithms are leading to state-of-the-art results. In this minisyposium we bring gather the leading researchers in this emerging arena to present a series of talks that will expose the current state of knowledge in this field. Organizers: Michael Elad (Technion, Israel)
|
|
|
|
Part I: Monday, July 7, 2008
|
10:30-10:55 Multidimensional Kernel Regression for Video Processing and Reconstruction Hiroyuki Takeda and Peyman Milanfar, University of California, Santa Cruz; Matan Protter and Michael Elad, Technion, Israel; Peter van Beek, Sharp Labs of America 11:00-11:25 A Discriminative Approach for Transform Based Restoration Yacov Hel-Or, The Interdisciplinary Center, Israel; Doron Shaked, HP Laboratories, Israel 11:30-11:55 Image Super-Resolution via Sparse Representation Jianchao Yang, John Wright, and Yi Ma, University of Illinois at Urbana-Champaign 12:00-12:25 Image Processing with Manifold Models Gabriel Peyre, Ceremade, France 12:30-12:55 Learning to Classify Guillermo Sapiro, University of Minnesota, Minneapolis
|
Part II: Wednesday, July 9, 2008
|
10:30-10:55 Generalizing the Non-Local-Means to Super-Resolution-Reconstruction of Image Sequences Michael Elad and Matan Protter, Technion, Israel; Hiro Takeda and Peyman Milanfar, University of California, Santa Cruz 11:00-11:25 Fast Super-Resolution of Video Sequences using Sparse Directional Transforms Sandeep Kanumuri and Onur Guleryuz, DoCoMo Communications Laboratories 11:30-11:55 Bayesian Non-local Means, Image Redundancy and Adaptive Estimation for Image Representation and Applications Charles Kervrann, IRISA, France; Patrick Perez, IRISA-INRIA, France; Jerome Boulanger, Institut Curie, France Alessandro Foi, Kostadin Dabov, Aram Danielyan, Karen Egiazarian and Vladimir Katkovnik, Tampere University of Technology, Finland |
Last Updated December 2, 2008 |