Cover Story：Zhao Y Y, Shi S X, et al. Light-field image super-resolution based on multi-scale feature fusion[J]. Opto-Electronic Engineering, 2020, 47(12): 200007

As a new generation of imaging equipment, a light-field camera can simultaneously capture the spatial position and incident angle of light rays. However, the recorded light-field has a trade-off between spatial resolution and angular resolution. Therefore, a light-field super-resolution network that fuses multi-scale features to obtain super-resolved light-field is proposed in this paper. The deep-learning-based network framework contains three major modules: multi-scale feature extraction module, global feature fusion module, and up-sampling module.The network proposed in this paper was applied to the synthetic light-field dataset and the real-world light-field dataset for light-field images super-resolution. The experimental results on the synthetic light-field dataset and real-world light-field dataset showed that this method outperforms other state-of-the-art methods in both visual and numerical evaluations.