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Paper Detail

Paper:	IMDSP-P17.7
Session:	Image Quality Assessment and Enhancement
Time:	Friday, May 19, 16:30 - 18:30
Presentation:	Poster
Topic:	Image and Multidimensional Signal Processing: Restoration and Enhancement
Title:	Empirical Conditional Mean: Nonparametric Estimator for Comparametric Exposure Compensation
Authors:	Dong Sik Kim, Su Yeon Lee, Hankuk University of Foreign Studies, Republic of Korea; Kiryung Lee, LG Electronnics Institue of Technology, Republic of Korea
Abstract:	In this paper, a comparametric exposure compensation is conducted using a nonparametric estimator: empirical onditional mean. The Nadaraya-Watson estimator is used to smooth the empirical conditional mean curve especially for the case of small number of samples. The performance of the estimator is compared with those of the polynomial and piecewise-linear fittings. Designing the Nadaraya-Watson estimator is very simple and achieves lower errors than the fitting cases, which require a heavy computational burden of solving equations, without worry about the singular matrix case.