Deep neural network for joint light field deblurring and super-resolution
Jonathan Samuel Lumentut, In Kyu Park · 2020
The recent works on the light field (LF) image enhancement are focused on specific tasks such as motion deblurring and super-resolution. State-of-the-art methods are limited with the specific case of 3-degree-of-freedom (3-DOF) camera motion (for motion deblurring) and straight-forward high-resolution neural network (for super-resolution (SR)). In this work, we proposed a framework that utilizes the deep neural net to solve LF spatial super- resolution and deblurring under 6-DOF camera motion. The neural network is designed with end-to-end fashion and trained in multiple stages to perform robust super-resolution and deblurring. Our neural network achieves superior results in terms of quantitative and qualitative performance compared to the recent state-of-the-art LF deblurring and SR algorithms.