in this paper, a class of signal-dependent noise models that are encountered in image processing applications is considered. Such models are uniquely defined by the gamma exponent, which rules the dependence on the signal, and by the variance of a zero-mean random noise process. An automatic procedure for measuring the model parameters directly from noisy images is presented. Then, adaptive filtering is applied in a multiresolution fashion, to take advantage of increasing SNR of the data at decreasing resolution. A rational Laplacian pyramid is generalized to the noise model to yield signal-independent noise on its layers. Experiments show a high accuracy of results, both of noise estimation and of filtering.
Signal-dependent noise modeling for adaptive multiresolution local-statistics filtering / Aiazzi, Bruno; Alparone, Luciano; Baronti, Stefano. - STAMPA. - 3646:(1999), pp. 207-216. (Intervento presentato al convegno Proceedings of the 1999 Nonlinear Image Processing X tenutosi a San Jose, CA, USA nel JAN 25-26, 1999) [10.1117/12.341087].
Signal-dependent noise modeling for adaptive multiresolution local-statistics filtering
ALPARONE, LUCIANO;
1999
Abstract
in this paper, a class of signal-dependent noise models that are encountered in image processing applications is considered. Such models are uniquely defined by the gamma exponent, which rules the dependence on the signal, and by the variance of a zero-mean random noise process. An automatic procedure for measuring the model parameters directly from noisy images is presented. Then, adaptive filtering is applied in a multiresolution fashion, to take advantage of increasing SNR of the data at decreasing resolution. A rational Laplacian pyramid is generalized to the noise model to yield signal-independent noise on its layers. Experiments show a high accuracy of results, both of noise estimation and of filtering.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.