Una comparación de reducción de ruido en imágenes digitales utilizando un modelado estadístico de coeficientes wavelet y filtrado de Wiener

Francisco  García Ugalde; Karina  Pérez Daniel; Laura  Reyes Ruíz; Manuel  Cedillo Hernández; Antonio  Cedillo Hernández; Mariko  Nakano Miyatake; Héctor  Pérez Meana

doi:10.24054/rcta.v2i30.168

Autores/as

Francisco García Ugalde Universidad Nacional Autónoma de México
Karina Pérez Daniel Universidad Nacional Autónoma de México
Laura Reyes Ruíz Universidad Nacional Autónoma de México
Manuel Cedillo Hernández Instituto Politécnico Nacional
Antonio Cedillo Hernández Instituto Politécnico Nacional
Mariko Nakano Miyatake Instituto Politécnico Nacional
Héctor Pérez Meana Instituto Politécnico Nacional

DOI:

https://doi.org/10.24054/rcta.v2i30.168

Palabras clave:

Disminución de ruido en imágenes digitales, Transformada wavelet, Filtrado de Wiener

Resumen

Este trabajo presenta un método de disminución de ruido en imágenes digitales, basado en un enfoque Bayesiano de dos etapas con ajuste empírico. Se estiman los coeficientes de una transformada wavelet de la imagen donde se ha reducido el ruido, utilizando una estimación lineal con un criterio de minimización del error cuadrático medio. Estos coeficientes constituyen una estimación deseable de la varianza de los coeficientes wavelet de la imagen libre de ruido.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Aja F., S., San José E., R., Alberola L., C. and Westin, C. F. (2006). “Image quality assessment based on local variance,” Proceedings of the 28th IEEE EMBC Annual International Conference, New York, August-September, SaBP2.17, pp. 4815-4818.

Arivazhagan, S., Sugitha, N. and Vijay, M. (2011). “Adaptive spatial and multiresolution approach for image denoising,” in Proceedings of International Conference on Signal Processing, Communication, Computing and Networking Technologies, ICSCCN, pp. 812-817.

Chang, S. G., Yu, B. and Vetterli, M. (2000). “Spatially adaptive wavelet thresholding with context modeling for image denoising,” IEEE Trans. Image Processing, Vol. 9, No. 9, pp. 1522-1531.

Jaiswal, A., Upadhyay, J. and Somkuwar, A. (2014). “Image denoising and quality measurements by using filtering and wavelet based techniques,” AUE-International Journal of Electronics and Communications, Vol. 68, No. 8, pp. 699-705.

Jaiswal, A. and Upadhyay, J. (2015). “An efficient approach for denoising of noises,” IEEE International Conference on Computer, Communication and Control, IC4, pp. 1-4.

Lo Presto, S., Ramchandran, K. and Orchard, M. T. (1997). “Image coding based on mixture modeling of wavelet coefficients and a fast estimation-quantization framework,” in Proc. IEEE Data Compression Conf., Snowbird, UT, pp. 221-230.

Mallat, S. (1989). “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. on Pattern Anal. Machine Intell., Vol. 11, No. 7, pp. 674-693.

Mihçak, M. K., Kozintsev, I. and Ramchandran, K. (1999). “Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising,” in Proc. Int. Conf. Acoustics, Speech, and Signal Processing, ICASSP99, Phoenix, AZ, Vol. 6, March, pp. 3253-3256.

Mihçak, M. K., Kozintsev, I., Ramchandran, K. and P. Moulin, (1999). “Low-complexity image denoising based on statistical modeling of wavelet coefficients,” IEEE Signal Processing Letters, Vol. 6, No. 12, pp 300-303.

Shapiro, J. M. (1993). “Embedded image coding using zerotrees of wavelet coefficients,” IEEE. Trans. on Signal Processing, Vol. 41, No. 12, pp. 3445-3462.

Wang, Z., Bovik, A. C., Sheikh, H. R. and Simoncelli, E. P. (2004). “Image quality assessment: From error visibility to structural similarity,” IEEE. Trans. on Image Processing, Vol. 13, No. 4, pp. 600-612.