Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-33718-5_40
Title: Nonuniform lattice regression for modeling the camera imaging pipeline
Authors: Lin, H.T.
Lu, Z.
Kim, S.J.
Brown, M.S. 
Issue Date: 2012
Source: Lin, H.T.,Lu, Z.,Kim, S.J.,Brown, M.S. (2012). Nonuniform lattice regression for modeling the camera imaging pipeline. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7572 LNCS (PART 1) : 556-568. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-33718-5_40
Abstract: We describe a method to construct a sparse lookup table (LUT) that is effective in modeling the camera imaging pipeline that maps a RAW camera values to their sRGB output. This work builds on the recent in-camera color processing model proposed by Kim et al. [1] that included a 3D gamut-mapping function. The major drawback in [1] is the high computational cost of the 3D mapping function that uses radial basis functions (RBF) involving several thousand control points. We show how to construct a LUT using a novel nonuniform lattice regression method that adapts the LUT lattice to better fit the 3D gamut-mapping function. Our method offers not only a performance speedup of an order of magnitude faster than RBF, but also a compact mechanism to describe the imaging pipeline. © 2012 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/39899
ISBN: 9783642337178
ISSN: 03029743
DOI: 10.1007/978-3-642-33718-5_40
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