Reducing Color and Noise Artifacts in Digital Cameras
Can We Get a Better Picture?
Images acquired by digital cameras are affected by sensor noise and by color deviations. It can be shown that there is a tradeoff between these two image artifacts which is dependent on the sensor sensitivities and the ensuing transformations applied to the sensor outputs.
We study sensors associated with a special class of cameras, namely, Colorimetric Cameras. These sensors are capable of capturing colors which span the human visual subspace, thus are able to produce zero Delta-E error, which in turn implies no color deviations in the captured image. We find the optimal sensors in this set with respect to minimizing sensor noise.
It was found that two of the three optimal sensors are surprisingly similar to the CIE-X and CIE-Z color matching functions. We show that this is not accidental. In fact, in choosing the XYZ primaries, the CIE unknowingly chose those whose matching functions (when considered as sensors) minimize sensor noise. We show that the criteria set by the CIE in choosing the XYZ primaries, imply minimization of sensor noise within our camera model.
Joint work with Ulrich Barnhoefer and Brian Wandell, Stanford University
The talk is self-contained.