Intelligent color temperature estimation using fuzzy neural network with application to automatic white balance

In this paper, a novel white balance method is proposed, which consists of two fundamental steps, i.e., color temperature estimation of the illuminant and adjustment of the components of the color separates. A fuzzy logic system is constructed to infer the color temperature of an illuminant which a digitally acquired image subjected to. The fuzzy logic system is further optimized by representing the system as a fuzzy neural network and a training scheme of the FNN parameters is proposed. The estimated color temperature is then employed to determine the required amount of adjustment for each color separate of the image. Experimental procedures are outlined and performed, which validates the feasibility and performance of the proposed method. A comparative study is also made based on two quantitative indices. The study shows that the proposed approach is preferable to most methods in the literatures in terms of performance and robustness to varieties of illuminants and scenes.