Oscillatory component recovery and separation in images by Sobolev norms

Applied and Computational Mathematics Seminar
Monday, November 29, 2010 - 13:00
1 hour (actually 50 minutes)
Skiles 255
University of California, Irvine
It has been suggested by Y. Meyer and numerically confirmed by many othersthat dual spaces are good for texture recovery. Among the dual spaces, ourwork focuses on Sobolev spaces of negative differentiability to recovertexture from noisy blurred images. Such Sobolev spaces are good to modeloscillatory component, on the other hand, the spaces themselves hardlydistinguishes texture component from noise component because noise is alsoconsidered to be a highly oscillatory component. In this talk, in additionto oscillatory component recovery, we will further investigate aone-parameter family of Sobolev norms to achieve such a separation task.