NDLI: Constrained likelihood ratios for detecting sparse signals in highly noisy 3D data

Content Provider	IEEE Xplore Digital Library
Author	Paris, S. Suleiman, R.F.R. Mary, D. Ferrari, A.
Copyright Year	2013
Description	Author affiliation: Lab. Lagrange, Univ. de Nice Sophia-Antipolis, Nice, France (Paris, S.; Suleiman, R.F.R.; Mary, D.; Ferrari, A.)
Abstract	We propose a method aimed at detecting weak, sparse signals in highly noisy three-dimensional (3D) data. 3D data sets usually combine two spatial directions x and y (e.g. image or video frame dimensions) with an additional direction λ (e.g. temporal, spectral or energy dimension). Such data most often suffer from information leakage caused by the acquisition system's point spread functions, which may be different and variable in the three dimensions. The proposed test is based on dedicated 3D dictionaries, and exploits both the sparsity of the data along the λ direction and the information spread in the three dimensions. Numerical results are shown in the context of astrophysical hyperspectral data, for which the proposed 3D model substantially improves over 1D detection approaches.
Starting Page	3947
Ending Page	3951
File Size	957378
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6638399
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Three-dimensional displays Dictionaries Hyperspectral imaging Vectors Data models Signal processing Noise measurement dictionary learning Detection sparse GLR hyperspectral
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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