Restoration model with inference capability of self-organizing maps

Abstract

This paper presents a restoration model with inference capability of self-organizing maps. Self-organizing maps have been studied principally for the ordering process and the convergence phase of weight vectors. As a novel approach of self-organizing maps, a restoration model for a defective image is proposed. The model creates a map containing one unit for each pixel. Utilizing pixel values as input, the inference for lost pixels is conducted by self-organizing maps. The inference of an original image proceeds appropriately since any pixel is influenced by neighboring pixels corresponding to the neighboring setting. Consequentially, images with high quality are constituted by restoring lost pixels. Experimental results are presented in order to show that our approach is effective in quality for restoration of lost pixels. © 2013 Springer-Verlag.