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SUMMARY:Image Denoising --- The SURE-LET Methodology  
DTSTART:20110624T141500
DTSTAMP:20260407T043330Z
UID:1fa16e9867e5e5988db81e562aad6c46912e37c6420a6b288af9b0f6
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Thierry Blu\, The Chinese University of Hong Kong\nThe g
 oal of this presentation is to promote a new approach for dealing with noi
 sy data --- typically\, images or videos here. Image denoising consists in
  approximating the noiseless image by performing some\, usually non-linear
 \, processing of the noisy image. Most standard techniques involve assumpt
 ions on the result of this processing (sparsity\, low high-frequency conte
 nts\, etc.)\; i.e.\, the denoised image. Instead\, the SURE-LET methodolog
 y consists in approximating the processing itself (seen as a function) ove
 r some linear combination of elementary non-linear processings (LET: Linea
 r Expansion of Thresholds)\, and to optimize the coefficients of this comb
 ination by minimizing a statistically unbiased estimate of the Mean Square
  Error (SURE: Stein's Unbiased Risk Estimate\, in the case of additive Gau
 ssian noise). I will introduce the technique and outline its advantages (f
 ast\, noise-robust\, flexible\, image adaptive). A comprehensive set of re
 sults will be shown and compared with the state-of-the-art.  Prof. Blu's h
 omepage
LOCATION:BC 01 https://plan.epfl.ch/?room==BC%2001
STATUS:CONFIRMED
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