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Proceedings Paper

Dictionaries for sparse representation and recovery of reflectances
Author(s): Steven Lansel; Manu Parmar; Brian A. Wandell
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Paper Abstract

The surface reflectance function of many common materials varies slowly over the visible wavelength range. For this reason, linear models with a small number of bases (5-8) are frequently used for representation and estimation of these functions. In other signal representation and recovery applications, it has been recently demonstrated that dictionary based sparse representations can outperform linear model approaches. In this paper, we describe methods for building dictionaries for sparse estimation of reflectance functions. We describe a method for building dictionaries that account for the measurement system; in estimation applications these dictionaries outperform the ones designed for sparse representation without accounting for the measurement system. Sparse recovery methods typically outperform traditional linear methods by 20-40% (in terms of RMSE).

Paper Details

Date Published: 17 February 2009
PDF: 11 pages
Proc. SPIE 7246, Computational Imaging VII, 72460D (17 February 2009); doi: 10.1117/12.813769
Show Author Affiliations
Steven Lansel, Stanford Univ. (United States)
Manu Parmar, Stanford Univ. (United States)
Brian A. Wandell, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 7246:
Computational Imaging VII
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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