Nonuniform lattice regression for modeling the camera imaging pipeline

Authors:
Hai Ting Lin;Zheng Lu;Seon Joo Kim;Michael S. Brown
Affiliations:
National University of Singapore, Singapore;University of Texas at Austin;SUNY, Korea;National University of Singapore, Singapore
Venue:
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Year:
2012

Citing 11
Cited 0

On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds

Mathematical Programming: Series A and B
Recovering high dynamic range radiance maps from photographs

Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Determining the Camera Response from Images: What Is Knowable?

IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Space of Camera Response Functions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the Radiometric Response Function from a Single Grayscale Image

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Robust Radiometric Calibration and Vignetting Correction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Priors for Large Photo Collections and What They Reveal about Cameras

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Radiometric calibration from a single image

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sequential linear interpolation of multidimensional functions

IEEE Transactions on Image Processing
From pixels to physics: Probabilistic color de-rendering

CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
A New In-Camera Imaging Model for Color Computer Vision and Its Application

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.