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)Peyman Milanfar (University of California, Santa Cruz)
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 AmericaRelated Papers:
H. Takeda, and P. Milanfar, M. Protter, M. Elad, “Superresolution Without Explicit Subpixel Motion Estimation ”, Submitted to IEEE Trans. Image Processing
H. Takeda, P. van Beek, and P. Milanfar, “Spatio-Temporal Video Interpolation and Denoising Using Motion-Assisted Steering Kernel (MASK) Regression”, Proceedings of IEEE International Conference on Image Processing (ICIP), San Diego, CA, October 2008.
11:00-11:25 A Discriminative Approach for Transform Based Restoration
Yacov Hel-Or, The Interdisciplinary Center, Israel; Doron Shaked, HP Laboratories, IsraelRelated Paper:
Y. Hel-Or and D. Shaked, "A Discriminative approach for Wavelet Shrinkage Denoising" IEEE Trans. on Image Processing, Vol 17, No. 4, April 2008
11:30-11:55 Image Super-Resolution via Sparse Representation
Jianchao Yang, John Wright, and Yi Ma, University of Illinois at Urbana-ChampaignRelated Paper:
Jianchao Yang, John Wright, and Yi Ma, "Image Super-Resolution as Sparse Representation of Raw Image Patches", CVPR, 2008, Anchorage, Alaska
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 CruzRelated Paper:
M. Protter, M. Elad, H. Takeda, and P. Milanfar, “Generalizing the Non-Local-Means to Super-resolution Reconstruction ”, accepted for publication in IEEE Trans. Image Processing, April 2008
11:00-11:25 Fast Super-Resolution of Video Sequences using Sparse Directional Transforms
Sandeep Kanumuri and Onur Guleryuz, DoCoMo Communications LaboratoriesRelated paper:
Sandeep Kanumuri, Onur G. Guleryuz, and M. Reha Civanlar, "Sparse directional transforms for superresolution reconstruction of mobile video," Proc. SPIE Conf. on Applications of Digital Image Processing XXX, San Diego, Aug. 2007.
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, FranceRelated Papers:
J. Boulanger, Ch. Kervrann, J. Salamero, J.-B. Sibarita, P. Bouthemy "Non-parametric regression for patch-based fluorescence microscopy image sequence denoisng" Rapport de recherche INRIA, No 6651, Septembre 2008
Alessandro Foi, Kostadin Dabov, Aram Danielyan, Karen Egiazarian and Vladimir Katkovnik, Tampere University of Technology, FinlandRelated Links:
Last Updated December 2, 2008