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Izvorni znanstveni članak
https://doi.org/10.2498/cit.2006.04.07

Image Recovery Using a New Nonlinear Adaptive Filter Based on Neural Networks

Armando De Giusti
Laura Lanzarini
German Osella Massa
Claudia Russo
Leonardo Corbalan

Puni tekst: engleski, pdf (385 KB) str. 315-320 preuzimanja: 445* citiraj
APA 6th Edition
De Giusti, A., Lanzarini, L., Osella Massa, G., Russo, C. i Corbalan, L. (2006). Image Recovery Using a New Nonlinear Adaptive Filter Based on Neural Networks. Journal of computing and information technology, 14 (4), 315-320. https://doi.org/10.2498/cit.2006.04.07
MLA 8th Edition
De Giusti, Armando, et al. "Image Recovery Using a New Nonlinear Adaptive Filter Based on Neural Networks." Journal of computing and information technology, vol. 14, br. 4, 2006, str. 315-320. https://doi.org/10.2498/cit.2006.04.07. Citirano 24.02.2020.
Chicago 17th Edition
De Giusti, Armando, Laura Lanzarini, German Osella Massa, Claudia Russo i Leonardo Corbalan. "Image Recovery Using a New Nonlinear Adaptive Filter Based on Neural Networks." Journal of computing and information technology 14, br. 4 (2006): 315-320. https://doi.org/10.2498/cit.2006.04.07
Harvard
De Giusti, A., et al. (2006). 'Image Recovery Using a New Nonlinear Adaptive Filter Based on Neural Networks', Journal of computing and information technology, 14(4), str. 315-320. https://doi.org/10.2498/cit.2006.04.07
Vancouver
De Giusti A, Lanzarini L, Osella Massa G, Russo C, Corbalan L. Image Recovery Using a New Nonlinear Adaptive Filter Based on Neural Networks. Journal of computing and information technology [Internet]. 2006 [pristupljeno 24.02.2020.];14(4):315-320. https://doi.org/10.2498/cit.2006.04.07
IEEE
A. De Giusti, L. Lanzarini, G. Osella Massa, C. Russo i L. Corbalan, "Image Recovery Using a New Nonlinear Adaptive Filter Based on Neural Networks", Journal of computing and information technology, vol.14, br. 4, str. 315-320, 2006. [Online]. https://doi.org/10.2498/cit.2006.04.07

Sažetak
This work defines a new nonlinear adaptive filter based on a feed-forward neural network with the capacity of significantly reducing the additive noise of an image. Even though measurements have been carried out using x-ray images with additive white Gaussian noise, it is possible to extend the results to other type of images. Comparisons have been carried out with theWeiner filter because it is the most effective option for reducing Gaussian noise. In most of the cases, image reconstruction using the proposed method has produced satisfactory results. Finally, some conclusions and future work lines are presented.

Hrčak ID: 44647

URI
https://hrcak.srce.hr/44647

Posjeta: 592 *