Auto white balance (AWB) is an important technique for digital cameras. Human vision system has the ability
to recognize the original color of an object in a scene illuminated by a light source that has a different color
temperature from D65-the standard sun light. However, recorded images or video clips, can only record the
original information incident into the sensor. Therefore, those recorded will appear different from the real scene
observed by the human. Auto white balance is a technique to solve this problem. Traditional methods such as
gray world assumption, white point estimation, may fail for scenes with large color patches. In this paper, an
AWB method based on color temperature estimation clustering is presented and discussed. First, the method
gives a list of several lighting conditions that are common for daily life, which are represented by their color
temperatures, and thresholds for each color temperature to determine whether a light source is this kind of
illumination; second, an image to be white balanced are divided into N blocks (N is determined empirically).
For each block, the gray world assumption method is used to calculate the color cast, which can be used to
estimate the color temperature of that block. Third, each calculated color temperature are compared with
the color temperatures in the given illumination list. If the color temperature of a block is not within any of
the thresholds in the given list, that block is discarded. Fourth, the remaining blocks are given a majority
selection, the color temperature having the most blocks are considered as the color temperature of the light
source. Experimental results show that the proposed method works well for most commonly used light sources.
The color casts are removed and the final images look natural.