NDLI: Fast binary image resolution increasing by k-nearest neighbor learning

Content Provider	IEEE Xplore Digital Library
Author	Hae Yong Kim Barreto, P.S.L.M.
Copyright Year	2000
Description	Author affiliation: Dept. Eng. Sistemas Eletronicos, Sao Paulo Univ., Brazil (Hae Yong Kim)
Abstract	In a typical office environment, heterogeneous devices and software, each working in a different spatial resolution, must interact together. Thus, there frequently arises the resolution conversion problem. The present paper addresses the spatial resolution increasing of binary images and documents (for example, conversion of a 300 dpi image into 600 dpi). A new and efficient solution to this problem is proposed. It makes use of k-nearest neighbor learning to automatically design the optimal windowed zoom operator starting from pairs of in-out sample images. The k-nearest neighbor learning has good inductive bias that allows reducing the quantity of the training sample images needed. The resulting operator is stored in a look-up-table, which is extremely fast computationally.
Starting Page	327
Ending Page	330
File Size	391746
Page Count	4
File Format	PDF
ISBN	0780362977
ISSN	15224880
DOI	10.1109/ICIP.2000.899376
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-09-10
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image resolution Spatial resolution Image converters Table lookup Digital images Software performance Gray-scale Printers Probability distribution Statistical distributions
Content Type	Text
Resource Type	Article

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