Title: Color Correction for Digital Cameras

Jeffrey M. DiCarlo

Department of Electrical Engineering

Stanford University

Brian A. Wandell

Department of Psychology

Stanford University

Abstract:

Several important problems in color imaging can be traced to differences in how cameras and humans sample the spectral properties of light. Color processing within the imaging pipeline, loosely referred to as color correction, transforms the sampled camera responses to a form that matches the human responses. The accuracy of the color correction transformation is limited because the human visual system and most color acquisition devices critically undersample the spectral information, making the differences in their sampling functions quite significant.

In this talk, we formulate color correction as an input-referred estimation problem. In particular, we analyze how a small number of camera measurements can be used to estimate a complete spectral surface reflectance function. We introduce conventional linear color transformations, and then extend these transformations using forms of local linear regression that we refer to as submanifold estimation methods. These methods are based on the observation that for many data sets the deviations between the signal and the linear estimate is systematic; submanifold methods incorporate knowledge of these systematic deviations to improve upon linear estimation methods. We describe the geometric intuition of these methods and evaluate the submanifold method on printed material data and hyperspectral image data.