A Feature-Based Approach for Light Field Video Enhancement

Light field imaging is increasingly spreading and its applications are reaching a larger number of users. The quality of the rendered content as perceived by the viewers is a key factor for the future development of this technology. Many factors can cause a degradation of the Light Field quality, from data acquisition/creation to the final rendering. To increase the perceived quality of the Light Field content, as usually performed for 2D and 3D content, processing techniques can be applied. In this work, an approach for enhancing Light Field videos is presented. The proposed approach is based on the characteristics of the human visual system and relies on the estimation of depth, motion and saliency. Those features guide a selective enhancement that is implemented in the Laguerre-Gauss Transform domain. The performed tests show the effectiveness of the proposed method.