Enlargement of digital images by using multi‐neural networks

Abstract

AbstractIn the enlargement of a digital image, it is required to estimate the high‐frequency components, which were lost in sampling. As a method of meeting this requirement, the authors have proposed an image enlargement method using a high‐resolution neural network (NN). The high‐resolution neural network provided excellent results of image enlargement. There remained, however, the following two problems. (1) When the image is enlarged by the high‐resolution NN, artifacts are produced in the smooth region of the enlarged image when the magnification ratio is increased as 4, 8, . . . . (2) There is a variation of approximately 10% in the accuracy of the enlarged result, depending on the kind of image used in the training of the high‐resolution NN. In order to cope with such problems, this paper newly proposes the high‐resolution multi‐neural network (MNN), which is based on the local variance. The training procedure is shown. In the high‐resolution MNN, two NN are used, corresponding to the region with a rapid change (high local variance) and the region requiring a smooth interpolation (low local variance), in the image for which resolution improvement is required. The weighted sum of the two NN outputs is defined as the result of enlargement. It is shown through application examples that the two problems in the high‐resolution NN are remedied by using the high‐resolution MNN. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 84(11): 61–69, 2001