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. © 1998 Scripta Technica, Electron Comm Jpn Pt 3, 82(1): 19–29, 1999
SUMMARYIn 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.
We have presented the enlargement neural network (NN) [3] for edge-preserving image interpolation, which is based on the non-linear procedure which is presented by Greenspan [2] . In this paper, we present a novel enlargement multi-neural network (MNN) which is an extended the enlargement NN. Simulation results show the superior performances of the proposed new approach with respect to other interpolation techniques as the enlargement NN and the Greenspan's method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.