DocumentCode :
767190
Title :
Efficient approximation of neural filters for removing quantum noise from images
Author :
Suzuki, Kenji ; Horiba, Isao ; Sugie, Noboru
Author_Institution :
Fac. of Inf. Sci. & Technol., Aichi Prefectural Univ., Japan
Volume :
50
Issue :
7
fYear :
2002
fDate :
7/1/2002 12:00:00 AM
Firstpage :
1787
Lastpage :
1799
Abstract :
In this paper, efficient filters are presented that approximate neural filters (NFs) that are trained to remove quantum noise from images. A novel analysis method is proposed for making clear the characteristics of the trained NF. In the proposed analysis method, an unknown nonlinear deterministic system with plural inputs such as the trained NF can be analyzed by using its outputs when the specific input signals are input to it. The experiments on the NFs trained to remove quantum noise from medical and natural images were performed. The results have demonstrated that the approximate filters, which are realized by using the results of the analysis, are sufficient for approximation of the trained NFs and efficient at computational cost
Keywords :
X-ray imaging; approximation theory; image sequences; learning (artificial intelligence); medical image processing; neural nets; nonlinear filters; nonlinear systems; quantum noise; reviews; approximate filters; computational cost; efficient approximation; input signals; medical X-ray image sequences; medical images; natural images; nonlinear deterministic system; nonlinear filters; quantum noise removal; trained neural filters; Biomedical imaging; Computational efficiency; Finite impulse response filter; Image enhancement; Neural networks; Noise measurement; Nonhomogeneous media; Nonlinear filters; Signal analysis; Signal processing;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2002.1011218
Filename :
1011218
Link To Document :
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