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.
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