Enlargement of digital images based on Laplacian pyramid by using neural networks

The enlargement of a digital image is a process of narrowing the sampling interval. Since the image before the enlargement and that after the enlargement have different Nyquist frequencies, it is necessary to predict or estimate the high-frequency components that are lost in the image before processing and to complement these components in the enlargement process. It is noted that the image can be represented as the sum of Gaussian components and Laplacian components in a hierarchical structure (pyramid representation). There exists a strong correlation between the Laplacian images of the layers. Based on that property, Greenspan and Anderson have proposed a nonlinear procedure where the unknown Laplacian image of high resolution (i.e., containing the lost high-frequency components) is estimated from the Laplacian image of low resolution. This paper notes that the Greenspan procedure can be network-structured and proposes an enlargement method for digital images using a neural network (NN) for improving the resolution, based on generalizing the network. It is shown that the enlarged NN can be constructed almost independently of the kind of training image or of the hierarchy (resolution). It is also shown that a better enlarged image can be derived compared with Greenspan's method. In other words, the effectiveness and practical usefulness of the proposed method are demonstrated. (C) 1998 Scripta Technica.