Binary operator design by k-nearest neighbor learning with application to image resolution increasing

被引:5
|
作者
Kim, HY [1 ]
机构
[1] Univ Sao Paulo, Dept Eng Sistemas Eletron, BR-05508900 Sao Paulo, Brazil
关键词
Image resolution;
D O I
10.1002/ima.1017
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In a typical office environment, heterogeneous devices and software, each working in a different spatial resolution, must interact. As a result resolution conversion problems arise frequently. This paper addresses the spatial resolution increasing of binary images and documents (e.g,, conversion of a 300-dots per inch [dpi] image into 600 dpi). A new, accurate and efficient solution to this problem is proposed. It makes use of the k-nearest neighbor learning to design automatically a windowed zoom operator starting from pairs of in-out sample images. The resulting operator is stored in a look-up table, which is extremely fast computationally and therefore fit for real-time applications. It Is useful to know a prior! the sample complexity (the quantity of training samples needed to get, with probability 1-delta, an operator with accuracy epsilon). We use the probably approximately correct (PAC) learning theory to compute sample complexity, for both noise-free and noisy cases. Because the PAC theory yields an overestimated sample complexity, the statistical estimation is used to estimate, a posteriori, a tight error bound. The statistical estimation is also used to show that the k-nearest neighbor learning has a good inductive bias that allows reduction of the quantity of training sample images needed. (C) 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 331-339, 2000.
引用
收藏
页码:331 / 339
页数:9
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