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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Uss, M. Vozel, B. Chehdi, K. Lukin, V.V. Abramov, S.K. |
| Copyright Year | 2010 |
| Description | Author affiliation: Dept of Transmitters, Receivers and Signal Processing, National Aerospace University, 17 Chkalova St, 61070, Kharkov, Ukraine (Lukin, V.V.; Abramov, S.K.) || University of Rennes I, 6, Rue de Kerampont, 22 305 Lannion Cedex, BP 80518, France (Uss, M.; Vozel, B.; Chehdi, K.) |
| Abstract | In this paper, we dealt with the problem of noise variance estimation from additive mixture of the noise and an underlying image texture. Assuming fBm-model for image texture, the number M(H,SNR)of SWs has been obtained such that statistical efficiency e of the previously designed ML noise variance estimator is close to a predefined level e = 0.9. The value M defines a boundary between asymptotic and non-asymptotic modes of the ML estimator with respect to image fragment size (number of SWs available). For fixed SNR, M takes minimum values for smooth textures ( H close to 0.8) and increases fast as H approaches 0. As a function of SNR, M has minimum at approximately SNR = 1.5 and increases fast as SNR deviates from this value. These results are useful for establishing the area of applicability of noise variance estimators and to assure the quality of estimates obtained from an image texture of a given size, roughness and SNR. |
| Starting Page | 1 |
| Ending Page | 3 |
| File Size | 419026 |
| Page Count | 3 |
| File Format | |
| ISBN | 9781424479009 |
| e-ISBN | 9781424478996 |
| DOI | 10.1109/MSMW.2010.5546161 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-21 |
| Publisher Place | Ukraine |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Signal to noise ratio Maximum likelihood estimation Image texture Correlation Covariance matrix |
| Content Type | Text |
| Resource Type | Article |
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