Abstract
This paper presents a novel image denoising framework using overcomplete topographic model. To adapt to the statistics of natural images, we impose sparseness constraints on the denoising model. Based on the overcomplete topographic model, our denoising system improves over previous work on the following aspects: multi-category based sparse coding, adaptive learning, local normalization, and shrinkage function. A large number of simulations have been performed to show the performance of the modified model, demonstrating that the proposed model achieves better denoising performance.
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Zhao, H., Luo, J., Huang, Z., Nagumo, T., Murayama, J., Zhang, L. (2013). Image Denoising Based on Overcomplete Topographic Sparse Coding. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_34
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DOI: https://doi.org/10.1007/978-3-642-42051-1_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
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