Fast Light Field Reconstruction Using Convolutional Neural Network to Double Angular Resolution
Ahmed Salem, Hatem Ibrahem, Hyun-Su Kang · 2020
Light field imaging is the best given the amount of information it provides when compared to conventional photography, it captures angular and spatial information from all directions. Despite that, its limited resolution poses great difficulty in the use of these enormous capabilities. In this paper, we tried to lessen the impact of this drawback by using a deep-learning algorithm. We adopted the idea of dividing the process into disparity estimation and color prediction. Our system was trained to double the angular resolution fast and accurately. Experimental results demonstrate that our system can reconstruct high-quality images faster than the state-of-the-art techniques.