Deep neural network for joint light field deblurring and super-resolution

Jonathan Samuel Lumentut; In Kyu Park

doi:10.1117/12.2566962

1 June 2020 Deep neural network for joint light field deblurring and super-resolution

Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 1151507 (2020) https://doi.org/10.1117/12.2566962
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

Abstract

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.

Citation Download Citation

Jonathan Samuel Lumentut and In Kyu Park "Deep neural network for joint light field deblurring and super-resolution", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 1151507 (1 June 2020); https://doi.org/10.1117/12.2566962

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