By Topic

Weighted overcomplete denoising

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Guleryuz, O.G. ; Epson Palo Alto Lab., CA, USA

We consider the familiar scenario where independent and identically distributed (i.i.d) noise in an image is removed using a set of overcomplete linear transforms and thresholding. Rather than the standard approach where one obtains the denoised signal by ad hoc averaging of the denoised estimates (corresponding to each transform), we formulate the optimal combination as a linear estimation problem for each pixel and solve it for optimal estimates. Our approach is independent of the utilized transforms and the thresholding scheme, and extends established work by exploiting a separate degree of freedom that is in general not reachable using previous techniques. Surprisingly, our derivation of the optimal estimates does not require explicit image statistics but relies solely on the assumption that the utilized transforms provide sparse decompositions. Yet it can be seen that our adaptive estimates utilize implicit conditional statistics and they make the biggest impact around edges and singularities where standard sparsity assumptions fail.

Published in:

Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on  (Volume:2 )

Date of Conference:

9-12 Nov. 2003