Synthetic aperture imaging using pixel labeling via energy minimization

Synthetic aperture imaging using an array of cameras, which has become popular recently, can easily handle the occlusion problem by “seeing through” occluders. Unfortunately, the resulting image is still blurry because it combines information not only from the region of interest but also from the occluding regions. Removing the blurriness of synthetic aperture images has become a challenging task for many computer vision applications. In this paper, we propose a novel method to improve the image quality of synthetic aperture imaging using energy minimization. Unlike the conventional synthetic aperture imaging method, which averages images from all the camera views, we reformulate the problem as a labeling problem. In particular, we use the energy minimization method to label each pixel in each camera view to decide whether or not it belongs to an occluder. After that, the focusing at the desired depth is by averaging pixels that are not labeled as occluder. The experimental results show that the proposed method outperforms the traditional synthetic aperture imaging method as well as its improved versions, which are simply dim and blur occluders in the resulting image. To the best of our knowledge, our proposed method is the first one for improving the results of synthetic aperture image without using a training set from the input sequence. As well, it is the first method that makes no assumptions on whether or not the objects in the scenes are static.