Abstract
A relaxation algorithm influenced by self-organizing maps for image restoration is presented in this study. Self-organizing maps have been hitherto studied for the ordering process and the convergence phase of weight vectors. As another approach of self-organizing maps, a novel algorithm of image restoration is proposed. The present algorithm creates a map containing one unit for each pixel. Utilizing pixel values as input, the image inference is carried out by self-organizing maps. Then, an updating function with a threshold is introduced, so as not to respond to a noisy input sensitively. Therefore, the inference of original image proceeds appropriately since any pixel is influenced by surrounding pixels corresponding to the neighboring setting. In the restoration process, the effect of the initial threshold and the initial neighborhood on accuracy is examined. Experimental results are presented in order to show that the present method is effective in quality.
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Maeda, M. (2003). A Relaxation Algorithm Influenced by Self-Organizing Maps. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_65
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DOI: https://doi.org/10.1007/3-540-44989-2_65
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