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|Title:||Nonuniform lattice regression for modeling the camera imaging pipeline|
|Citation:||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.  that included a 3D gamut-mapping function. The major drawback in  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)|
|Appears in Collections:||Staff Publications|
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