Browsing by Subject "Blind deconvolution"

Now showing 1 - 3 of 3

Open Access
Convexity in source separation: Models, geometry, and algorithms
(Institute of Electrical and Electronics Engineers Inc., 2014) McCoy, M. B.; Cevher, V.; Dinh, Q. T.; Asaei, A.; Baldassarre, L.
Source separation, or demixing, is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction, blind deconvolution, and even dictionary learning. Despite the recent progress in each of these applications, advances in high-throughput sensor technology place demixing algorithms under pressure to accommodate extremely high-dimensional signals, separate an ever larger number of sources, and cope with more sophisticated signal and mixing models. These difficulties are exacerbated by the need for real-time action in automated decision-making systems. © 1991-2012 IEEE.
Open Access
Exact blind channel estimator
(1998) Özdemir, Ahmet Kemal
Recently blind identification of single-input multiple-output (SIMO) FIR channels has received considerable attention. The obtained exact identification approaches place over-restrictive constraints on the channels. In this thesis least set of constraints on the channels are placed and the noise-free blind channel identification problem is solved in two stages: The identification of the uncommon zeros followed by the identification of the common zeros of the channels. The minimum number of samples required to identify the uncommon zeros is specified, and closed form solutions are obtained. Also a binary-tree algorithm is proposed for the computation of the uncommon zeros efficiently. Then the common zeros of the channels are identified by a novel pruning algorithm. Finally a simulation example is presented to illustrate these ideas.
Open Access
Phase and TV based convex sets for blind deconvolution of microscopic images
(Institute of Electrical and Electronics Engineers Inc., 2016) Tofighi M.; Yorulmaz, O.; Köse K.; Yıldırım, D. C.; Çetin-Atalay R.; Çetin, A. Enis
In this paper, two closed and convex sets for blind deconvolution problem are proposed. Most blurring functions in microscopy are symmetric with respect to the origin. Therefore, they do not modify the phase of the Fourier transform (FT) of the original image. As a result blurred image and the original image have the same FT phase. Therefore, the set of images with a prescribed FT phase can be used as a constraint set in blind deconvolution problems. Another convex set that can be used during the image reconstruction process is the Epigraph Set of Total Variation (ESTV) function. This set does not need a prescribed upper bound on the Total Variation (TV) of the image. The upper bound is automatically adjusted according to the current image of the restoration process. Both the TV of the image and the blurring filter are regularized using the ESTV set. Both the phase information set and the ESTV are closed and convex sets. Therefore they can be used as a part of any blind deconvolution algorithm. Simulation examples are presented.