Abstract
A blind image deblurring method based on a new non-gaussianity measure and independent component analysis is presented. The scheme assumes independency among source signals (image and filter function) in the frequency domain. According to the Central Limit Theorem the blurred image becomes more Gaussian. The original image is assumed to be non-gaussian and using a spectral non-gaussianity measure (kurtosis or negentropy) one can estimate an inverse filter function that maximizes the non-gaussianity of the deblurred image. A genetic algorithm (GA) optimizing the kurtosis in the frequency domain is used for the deblurring process. Experimental results are presented and compared with some existing methods. The results show that the deblurring from the spectral domain offers several advantages over that from the spatial domain.
This is a preview of subscription content, log in via an institution to check access.
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
Oppenheim, A.V., Schafer, R.W., Stockham, T.G.: Nonlinear Filtering Of Multiplied and Convolved Signals. Proceedings of the Institute of Electrical and Electronics Engineers 56, 1264 (1968)
Stockham, T.G., Cannon, T.M., Ingebretsen, R.B.: Blind Deconvolution through Digital Signal-Processing. Proc. IEEE 63, 678–692 (1975)
Richardson, W.H.: Bayesian-Based Iterative Method of Image Restoration. Journal of the Optical Society of America 62, 55 (1972)
Chan, T.F., Wong, C.K.: Total Variation Blind Deconvolution. IEEE Trans. Image Process. 7, 370–375 (1998)
Wiggins, R.A.: Minimum Entropy Deconvolution. Geoexploration 16, 21–35 (1978)
Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley-Interscience Publication, Hoboken (2001)
Comon, P., Jutten, C.: Handbook of Blind Source Separation (Independent Component Analysis and Applications). Elsevier, Amsterdam (2010)
Yin, H.J., Hussain, I.: Independent Component Analysis and Non-Gaussianity for Blind Image Deconvolution and Deblurring. Integr. Comput.-Aided Eng. 15, 219–228 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khan, A., Yin, H. (2011). Spectral Non-gaussianity for Blind Image Deblurring. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-23878-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23877-2
Online ISBN: 978-3-642-23878-9
eBook Packages: Computer ScienceComputer Science (R0)