Adaptive Image Backlight Compensation for Mobile Phone Users

The user-friendliness and cost-effectiveness have contributed to the growing popularity of mobile phone cameras. However, images captured by such mobile phone cameras are easily distorted by a wide range of factors, such as backlight, over-saturation, and low contrast. Although several approaches have been proposed to solve the backlight problems, most of them still suffer from distorted background colors and high computational complexity. Thus, they are not deployable in mobile applications requiring real-time processing with very limited resources. In this paper, we present a novel framework to compensate image backlight for mobile phone applications based on an adaptive pixel-wise gamma correction which is computationally efficient. The proposed method is composed of two sequential stages: 1) illumination condition identification and 2) adaptive backlight compensation. Given images are classified into facial images and non-facial images to provide prior knowledge for identifying the illumination condition at first. Then we further categorize the facial images into backlight images and nonbacklight images based on local image statistics obtained from corresponding face regions. We finally compensate the image backlight using an adaptive pixel-wise gamma correction method while preserving global and local contrast effectively. To show the superiority of our algorithm, we compare our proposed method with other state-of-the-art methods in the literature.