A Multiscale Neural Network Method for Image Restoration

A.P.A. de Castro, I.N. Drummond, J.D.S. da Silva

Abstract


This paper describes a novel neural network based multiscale image restoration approach. The method uses a Multilayer Perceptron (MLP) trained with synthetic gray level images of artificially degraded co-centered circles. The main difference of the present approach to existing ones relies on the fact that the space relations are used and they are taken from different scales, which makes it possible for the neural network to establish space relations among the considered pixels in the image. This approach attempts at coming up with a simple method that leads to an optimum solution to the problem without the need to establish a priori knowledge of existing noise in the images. The multiscale data is acquired by considering different window sizes around a pixel. The performance of the proposed approach is close to existing restoration techniques but it was observed that the resulting images showed a slight increase in contrast and brightness. The proposed technique is also used as a preprocessing phase in a real-life classification problem of medical Magnetic Resonance Images (MRI) by using a fuzzy classification technique.

References


[1] M. Bertero, P. Boccacci, “Introduction to Inverse Problems in Imaging”, Philadelphia, Bristol, 1998.

J. Bezdek, R. Ehrlich, W. Full, FCM: The fuzzy c-means algorithm. Computers & Geosciences, 10, No. 2-3, (1984), 491–263.

Y.M. Bishop, S.E. Feinberg, P.W. Holland, “Discrete Multivariate Analysis: Theory and Practice”, Cambridge: MIT Press, 1975.

A.P.A. Castro, J.D.S. Silva, Neural Network-Based Multiscale Image Restoration Approach. In: Proceeding on Electronic Imaging, Vol. 6497, San Jose, pp. 3854–3859, 2007.

A.P.A. Castro, J.D.S. Silva, Neural Network-Based Multiscale Image Restoration Approach. In: Proceedings of IPDO, Miami, 2007.

J. Chen, J. Benesty, Y. Huang, S. Doclo, New insights into the noise reduction wiener filter, IEEE Trans. on Audio, Speech and Language Processing, 14, No.4 (2006), 1218–1234.

I. Drummond, S. Sandri, A clustering-based possibilistic method for image classification, Lecture Notes in Computer Science, 3171 (2004), 454–463.

I. Drummond, S. Sandri, A clustering-based fuzzy classifier, Frontiers in Artificial Intelligence and Applications, 131, No. 1 (2005), 247–254.

R.C. Gonzalez, R.C. Woods, “Digital Image Processing”, New York, Addison Wesley, 1992.

S. Haykin, “Redes Neurais: Princ´ıpios e Pr´atica”, P. Alegre, Bookman, 2001.

K.V.D. Heijden, “Image BasedMeasurement Systems”, New York,Wiley, 1994.

A.K. Jain, “Fundamentals of Digital Image Processing”, New Jersey, Prentice Hall, Inc, 1989.

A.D. Kulkarni, “Computer Vision and Fuzzy-Neural Systems”, New Jersey, Prentice Hall, 2001.

Y.D. Wu, Q.Z. Zhu, S.X. Sun, H.Y. Zhang, Image restoration using variational PDE-based neural network, Neurocomputing, 69, No. 16-18, (2006), 2364–2368.




DOI: https://doi.org/10.5540/tema.2008.09.01.0041

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.



TEMA - Trends in Applied and Computational Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)
ISSN: 1677-1966  (print version),  2179-8451  (online version)

Indexed in:

                        

 

Desenvolvido por:

Logomarca da Lepidus Tecnologia