Improving the Auto White Balance Result by Using Image Characteristics

  • 莊 宗達

Student thesis: Master's Thesis

Abstract

In image processing auto white balance(AWB) is a function used for color correction under different color temperatures so that the result looks like under canonical illumination The traditional auto white balance methods can not correct color cast in some situations For the Gray World theory may lose color richness after execution and the over-contrast problem for the Color Histogram Stretching method In this thesis an auto white balance algorithm based on image characteristics has been proposed combining the Gray World algorithm and the Color Histogram Stretching algorithm improving the shortcomings of the two algorithms and preserving their respective advantages The architecture of the proposed algorithm composed of three main parts: Firstly the input image is processed by two different auto white balance algorithms and gets two derived inputs Secondly the characteristics of these two inputs are extracted and then are calculated the weights according to these characteristics Finally the result is obtained by weighted fusing of input images The experiment results demonstrate that the proposed algorithm can more accurately correct the color cast image and outperform other auto white balance algorithms in the comparison of the visual quality and the numerical comparisons
Date of Award2015 Jul 29
Original languageEnglish
SupervisorShen-Chuan Tai (Supervisor)

Cite this

'