create_variation_model (640, 480, 'byte', 'standard', ModelID1) * * Training of the Variation Model * gauss_distribution (20, Distribution) for i := 1 to 50 by 1 add_noise_distribution (ImageReduced, ImageNoise, Distribution) train_variation_model (ImageNoise, ModelID1) endfor