REPAID: resolution-enhanced plenoptic all-in-focus imaging using deep neural networks.

Abstract

Due to limited depth-of-focus, classical 2D images inevitably lose details of targets out of depth-of-focus, while all-in-focus images break through the limit by fusing multi-focus images, thus being able to focus on targets in extended depth-of-view. However, conventional methods can hardly obtain dynamic all-in-focus imaging in both high spatial and temporal resolutions. To solve this problem, we design REPAID, meaning resolution-enhanced plenoptic all-in-focus imaging using deep neural networks. In REPAID, multi-focus images are first reconstructed from a single-shot plenoptic image, then upsampled using specially designed deep neural networks suitable for real scenes without ground truth to finally generate all-in-focus image in both high temporal and spatial resolutions. Experiments on both static and dynamic scenes have proved that REPAID can obtain high-quality all-in-focus imaging when using simple setups only; therefore, it is a promising tool in applications especially intended for imaging dynamic targets in large depth-of-view.