Noise Reduction and Image Sharpening Using IJA Cellular Learning Automaton
Amir Nooraliei, R. Iraji · 2010
This paper utilizes IJA stochastic learning automaton for detecting noise and tuning value of alpha parameter which is used for image sharpening via gas diffusion model. The method has been applied to gray-scale images in an automatic and adaptive fashion. It is shown that the IJA automaton detects noise and can reform it appropriately. It glides the image to find the pattern of noise and replace it by the relevant characteristics of neighborhood to carry out the local restoration. Then, the automaton makes the image sharp with gas diffusion model by learning alpha parameter. The IJA automaton calculates appropriate local value for each pixel. Finally, experiments are presented and comparisons with other common used techniques are introduced which illustrate the proposed approach produces excellent results for the problem of restoring gray-scale images.