Oscillatory component recovery and separation in images by Sobolev norms
Professor Yunho Kim
Department of Mathematics
UC Irvine
ABSTRACT
It has been suggested by Y. Meyer and numerically confirmed by many
others that dual spaces are good for texture recovery. Among the dual
spaces, our work focuses on Sobolev spaces of negative
differentiability to recover texture from noisy blurred images. Such
Sobolev spaces are good to model oscillatory component, on the other
hand, the spaces themselves hardly distinguishes texture component
from noise component because noise is also considered to be a highly
oscillatory component. In this talk, in addition to oscillatory
component recovery, we will further investigate a one-parameter family
of Sobolev norms to achieve such a separation task.
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