By Topic

Noninvertible Gabor Transforms

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
$33 $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

3 Author(s)
Ewa Matusiak ; Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa, Israel ; Tomer Michaeli ; Yonina C. Eldar

Time-frequency analysis, such as the Gabor transform, plays an important role in many signal processing applications. The redundancy of such representations is often directly related to the computational load of any algorithm operating in the transform domain. To reduce complexity, it may be desirable to increase the time and frequency sampling intervals beyond the point where the transform is invertible, at the cost of an inevitable recovery error. In this paper we initiate the study of recovery procedures for noninvertible Gabor representations. We propose using fixed analysis and synthesis windows, chosen e.g., according to implementation constraints, and to process the Gabor coefficients prior to synthesis in order to shape the reconstructed signal. We develop three methods for signal recovery. The first follows from the consistency requirement, namely that the recovered signal has the same Gabor representation as the input signal. The second, is based on minimization of a worst-case error. Last, we develop a recovery technique based on the assumption that the input signal lies in some subspace of L2 . We show that for each of the criteria, the manipulation of the transform coefficients amounts to a 2D twisted convolution, which we show how to perform using a filter-bank. When the undersampling factor is an integer, the processing reduces to standard 2D convolution. We provide simulation results demonstrating the advantages and weaknesses of each of the algorithms.

Published in:

IEEE Transactions on Signal Processing  (Volume:58 ,  Issue: 5 )