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SUMMARY:High-Dimensional Mixture Models For Unsupervised Image Denoising (
 HDMI) - Julie Delon (Université Paris Descartes)
DTSTART:20171101T111000Z
DTEND:20171101T120000Z
UID:TALK94255@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:This work addresses the problem of patch-based image denoising
  through the unsupervised learning of a probabilistic high-dimensional mix
 ture models on the noisy patches. The model\, named HDMI\, proposes a full
  modeling of the process that is supposed to have generated the noisy patc
 hes. To overcome the potential estimation problems due to the high dimensi
 on of the patches\, the HDMI model adopts a parsimonious modeling which as
 sumes that the data live in group-specific subspaces of low dimensionaliti
 es. This parsimonious modeling allows in turn to get a numerically stable 
 computation of the conditional expectation of the image which is applied f
 or denoising. The use of such a model also permits to rely on model select
 ion tools to automatically determine the intrinsic dimensions of the subsp
 aces and the variance of the noise. This yields a blind denoising algorith
 m that demonstrates state-of-the-art performance\, both when the noise lev
 el is known and unknown.   Joint work with Charles Bouveyron and Antoine H
 oudard.
LOCATION:Seminar Room 1\, Newton Institute
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