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Nonuniform Lattice Regression for Modeling the Camera Imaging Pipeline

  • Hai Ting Lin
  • Zheng Lu
  • Seon Joo Kim
  • Michael S. Brown
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

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.

Keywords

Node Level Radiometric Calibration Lattice Regression Uniform Lattice Pixel Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hai Ting Lin
    • 1
  • Zheng Lu
    • 2
  • Seon Joo Kim
    • 3
  • Michael S. Brown
    • 1
  1. 1.National University of SingaporeSingapore
  2. 2.University of Texas at AustinUSA
  3. 3.SUNYKorea

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