Abstract
In this paper we present an automatic color correction framework based on memory colors. Memory colors for 3 different objects: grass, snow and sky are obtained using psychophysical experiments under different illumination levels and later modeled statistically. While supervised image segmentation method detects memory color objects, a luminance level predictor classifies images as dark, dim or bright. This information along with the best memory color model that fits to the data is used to do the color correction using a novel weighted Von Kries formula. Finally, a visual experiment is conducted to evaluate color corrected images. Experimental results suggest that the proposed weighted von Kries model is an appropriate color correction model for natural images.