Synthetic aperture image quality assessment based on camera array: Measures and their performance

Synthetic aperture imaging, which is very powerful method for “seeing through” occlusion, has been proposed recently. It not only makes the object on the focal plane clear and sharp, but also improves signal-to-noise ratio of the result image. Although there are a lot of image quality assessment metrics for evaluating the image quality, unfortunately they are seldom used for evaluating the quality of synthetic aperture image. In this paper, we use some general image quality assessment metrics including: mean, variance, entropy, mean squared error, peak signal-to-noise ratio, cross entropy and correlation coefficient to evaluate the quality of synthetic aperture image. The main characteristics of this paper include three parts: (1) Systematically evaluating the quality of synthetic aperture image by several widely used image quality metrics. (2) Determine the performance of these metrics on synthetic aperture image. (3) Using some good performance metrics on autofocusing to determine on which focal plane is hidden object.