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.
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© 2006 Springer-Verlag Berlin Heidelberg
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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
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DOI: https://doi.org/10.1007/11760023_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
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