Aliasing detecting of light field images based on random masked aperture

In a light field imaging system, the angular down-sampling problem will lead to severe aliasing effects, which can significantly deteriorate the quality of a light field image. To address this problem, first we model the causes of aliasing effects in a 2D light field framework. Then, we propose a random masked aperture (RMA) based aliasing detecting algorithm. We use the coefficient of variations of imaging set derived from random masked aperture as an aliasing metric. Most importantly, the proposed algorithm is free of depth estimation and texture independent. We have validated the proposed algorithm on several groups of real light field datasets, which are acquired by using a planar camera array system. Finally, we alleviate the aliasing artifacts by employing the detecting results.