End-to-end snapshot compressed super-resolution imaging with deep optics

Abstract

Capturing both fine structure and high dynamics of the scene is demanded in many applications. However, such high throughput recording requires significant transmission bandwidth and large storage. Off-the-shelf super-resolution and temporal compressive sensing can partially address this challenge, but directly concatenating the two techniques fails to boost throughput because of artifact accumulation and magnification in sequential reconstruction. In this Letter, we propose an encoded capturing approach to simultaneously increase spatial and temporal resolvability with a low-bandwidth camera sensor. Specifically, we introduce point-spread-function (PSF) engineering via deep optics to encode fine spatial details and temporal compressive sensing to encode fast motions into a low-resolution snapshot. Furthermore, we develop an end-to-end deep neural network to optimize PSF and retrieve high-throughput videos from a compactly compressed measurement. Trained on simulation data and fine-tuned to fit system settings, our end-to-end system offers a 128 × data throughput compared to conventional imaging.