Abstract
Blur identification is one important part of image restoration process. Linear motion blur is one of the most common degradation functions that corrupts images. Since 1976, many researchers tried to estimate motion blur parameters and this problem is solved in noise free images but in noisy images improvement can be done when image SNR is low. In this paper we have proposed a method to estimate motion blur parameters such as direction and length using Radon transform and Feed-Forward back propagation neural network for noisy images. To design the desired neural network, we used Weierstrass approximation theorem and Steifel reference Sets. The experimental results showed algorithm precision when SNR is low and they were very satisfactory.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Moghaddam, M.E., Jamzad, M.: Blur identification in noisy images using radon transform and power spectrum modeling. In: IEEE 12th International workshop on systems,signal and image processing(IWSSIP), Greece, Chalkida (2005)
Li, Q., Yoshida, Y.: Parameter Estimation and Restoration for motion blurred Images. IEICE Trans. Fundamentals E80-A(8) (August 1997)
Chang, M.M., Tekalp, A.M., Erdem, A.T.: Blur identification using the bispectrum. IEEE Trans. Acoust., Speech, Signal Processing 39 (October 1991)
Cannon, M.: Blind deconvolution of spatially invariant image blurs with phase. IEEE Trans. Acoust., Speech, Signal Processing 24, 58–63 (1976)
Rekleities, I.M.: Optical Flow recognition from the power spectrum of a single blurred image. In: ICIP (1996)
Mayntz, C., Aach, T., Kunz, D.: Blur Identification using a spectral Inertial Tensor and Spectral zeros. In: ICIP (1999)
He, W.-G., Shao-Fali, Hu, F.-W.: Blur identification using adaptive adaline network. In: IEEE international conference on machine learning and cybernetics, Guangzhou, August 18-21 (2005)
Cho, C.-M., Don, H.-S.: Blur identification and image restoration using a multilayer neural network. In: IEEE international joint conference on Neural Networks (1991)
Yap, K.-H., Guan, L.: A recursive approach to joint image restoration and compensated blur identification. In: IEEE international society workshop on neural netwoks for signal processing, pp. 11–13 (December 2000)
Todd, J.: Introduction to the constructive theory of functions. California Institute of Technology (1961)
Powell, M.J.D.: Approximation Theory and Methods. Cambridge University Press, Cambridge (1981)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moghaddam, M.E., Jamzad, M., Mahini, H.R. (2006). Motion Blur Identification in Noisy Images Using Feed-Forward Back Propagation Neural Network. In: Zheng, N., Jiang, X., Lan, X. (eds) Advances in Machine Vision, Image Processing, and Pattern Analysis. IWICPAS 2006. Lecture Notes in Computer Science, vol 4153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11821045_39
Download citation
DOI: https://doi.org/10.1007/11821045_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37597-5
Online ISBN: 978-3-540-37598-2
eBook Packages: Computer ScienceComputer Science (R0)