TWO-ILLUMINANT ESTIMATION AND USER-PREFERRED CORRECTION FOR IMAGE COLOR CONSTANCY

Abstract

Illuminant estimation and correction (white-balance) is a fundamental process in camera image processing pipelines. This thesis examines the problem of white-balance when a scene contains two illuminants. This is a two-step process: 1) estimate the two illuminants; and 2) correct the image. Existing methods addressing this problem attempt to estimate multiple illuminants to produce a spatially varying illumination map. However, their results are still error prone and the resulting illumination maps are too low-resolution to be used for proper spatially varying white-balance correction. In addition, the spatially varying nature of these methods make them computationally intensive.

Our approach is to show that this problem can be effectively addressed by not attempting to obtain a spatially varying illumination map, but instead by detecting and estimating two illuminants, namely indoor and outdoor illuminants, by performing single illuminant estimation on large sub-regions of the image. Our approach is able to detect when distinct illuminants are present in the image and accurately measure these illuminants. Since our proposed strategy is not suitable for spatially varying image correction, two user studies have been performed to see if there is a preference for how the image should be corrected when two illuminants are present, but only a global correction can be applied. The user studies show that when the illuminants are distinct, there is a preference for the outdoor illuminant to be corrected resulting in warmer final image. We use these collective findings to demonstrate an effective two illuminant estimation scheme that produces corrected images that users prefer.