Adaptive Cycle Spinning Cellular Neural Network for Image Resolution Enhancement

Abstract

Cycle spinning cellular neural networks (CS-CNNs) are artificial neural networks that work effectively to solve large-scale problems. In our previous work, a CS-CNN is applied to enhance the resolution of images with an arbitrary magnification parameter. In this paper, a novel adaptive architecture using a CS-CNN is developed to prevent the unnecessary smoothing of image detail. While a discrete-time cellular neural network (DT-CNN) transforms all pixel values into coefficients to predict the original pixel values using the A-template, the adaptive cycle spinning method with "minmod" functions is applied to estimate the optimal coefficients from individual outputs of the DT-CNN as above. The minmod functions are defined on the basis of the interpolation error theorem. Experimental results indicate that the proposed method produces better results than the conventional image resolution enhancement methods.