Authors:
Michiharu Maeda
1
;
Noritaka Shigei
2
and
Hiromi Miyajima
2
Affiliations:
1
Fukuoka Institute of Technology, Japan
;
2
Kagoshima University, Japan
Keyword(s):
Self-organizing maps, Neighboring inputs, Image restoration, Degraded image.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neuroinformatics and Bioinformatics
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This paper presents learning algorithms with neighboring inputs in self-organizing maps for image restoration. Novel approaches are described that neighboring pixels as well as a notice pixel are prepared as an input, and a degraded image is restored according to an algorithm of self-organizing maps. The algorithm creates a map containing one unit for each pixel. Utilizing pixel values as input, image inference is conducted by selforganizing maps. An updating function with threshold according to the difference between input value and inferred value is introduced, so as not to respond to noisy input sensitively. The inference of an original image proceeds appropriately since any pixel is influenced by neighboring pixels corresponding to the neighboring setting. Experimental results are presented in order to show that our approaches are effective in quality for image restoration.