Abstract
This paper designs an edge preserving regularization model for image restoration. First, we propose a generalized form of Digitized Total Variation (DTV), and then introduce it into restoration model as the regularization term. To minimize the proposed model, we map digital image onto network, and then develop energy descending schemes based on Hopfield neural network. Experiments show that our model can significantly better preserve the edges of image compared with the commonly used Laplacian regularization (with constant and adaptive coefficient). We also study the effects of neighborhood and gaussian parameter on the proposed model through experiments.
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.
Similar content being viewed by others
References
Zhou, Y.T., Chellappa, R., Vaid, A., Jenkins, B.K.: Image Restoration Using a Neural Network. IEEE Trans. on Acoustics, Speech, and Signal Processing 36(7), 1141–1151 (1988)
Palik, J.K., Katsaggelos, A.K.: Image Restoration Using a Modified Hopfield Network. IEEE Transactions on Image Processing 1(1), 49–63 (1992)
Sun, Y.: Hopfield Neural Netwoks Based Algorithms for Image Restoration and Reconstruction–Part I: Algorothms and Simulations. IEEE Trans. on Signal Processing 48(7), 2105–2118 (2000)
Perry, S.W., Guan, L.: Weight Assignment for Adaptive Image Restoration by Neural Networks. IEEE Trans. on Neural Network 11(1), 156–170 (2000)
Ghennam, S., Benmahammed, K.: Adaptive Image Restoration Using Hopfield Neural Network. In: Proceedings of the 2001 IEEE Signal Processing Society Workshop, pp. 569–578 (2001)
Palmer, A.S., Razaz, M., Mandic, D.P.: Spatially Adaptive Image Restoration by Neural Network Filtering. In: Proceedings of VII Brazilian Symposium on Neural Networks, pp. 184–189 (2002)
Wong, H.S., Guan, L.: Adaptive Regularization in Image Restoration Using Model-Based Neural Network. Optical Engineering 36(12), 3297–3308 (1997)
Kang, M.G., Katsaggelos, A.K.: General Choice of the Regularization Functional in Regularized Image Restoration. IEEE Trans. on Image Processing 4(5), 594–602 (1995)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear Total Variation Based Noise Removal Algorithms. Physica D 60(1-4), 259–268 (2001)
Chan, T.F., Osher, S., Shen, J.H.: The Digital TV Filter and Nonlinear Denoising. IEEE Trans. on Image Processing 10(2), 231–241 (2001)
Chan, T.F., Mulet, P.: On the Convergence of the Lagged Diffusivity Fixed Point Method in Total Vatiation Image Restoration. SIAM Journal of Numerical Analysis 36(2), 354–367 (1999)
Perry, S.W., Guan, L.: Perception-Based Adaptive Image Restoration. In: Proc. Int. Conf. Acoust., Speech, Signal Processing, pp. 2893–2896 (1998)
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
Sun, J., Xu, Z. (2006). An Edge Preserving Regularization Model for Image Restoration Based on Hopfield Neural Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_83
Download citation
DOI: https://doi.org/10.1007/11760023_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)