Bayesian Correction of Image Intensity with Spatial Consideration

Under dimly lit condition, it is difficult to take a satisfactory image in long exposure time with a hand-held camera. Despite the use of a tripod, moving objects in the scene still generate ghosting and blurring effect. In this paper, we propose a novel approach to recover a high-quality image by exploiting the tradeoff between exposure time and motion blur, which considers color statistics and spatial constraints simultaneously, by using only two defective input images. A Bayesian framework is adopted to incorporate the factors to generate an optimal color mapping function. No estimation of PSF is performed. Our new approach can be readily extended to handle high contrast scenes to reveal fine details in saturated or highlight regions. An image acquisition system deploying off-the-shelf digital cameras and camera control softwares was built. We present our results on a variety of defective images: global and local motion blur due to camera shake or object movement, and saturation due to high contrast scenes.