Learn to See Faster: Pushing the Limits of High-Speed Camera with Deep Underexposed Image Denoising
This addresses the problem of capturing high-fidelity videos of fast-moving phenomena for researchers and engineers, but it is incremental as it adapts existing deep learning methods to a specific application.
The paper tackled the trade-off between motion blur and underexposure noise in high-speed imaging by treating it as an underexposed image denoising problem, achieving a speedup of over one order of magnitude in acquisition rate while maintaining similar image quality.
The ability to record high-fidelity videos at high acquisition rates is central to the study of fast moving phenomena. The difficulty of imaging fast moving scenes lies in a trade-off between motion blur and underexposure noise: On the one hand, recordings with long exposure times suffer from motion blur effects caused by movements in the recorded scene. On the other hand, the amount of light reaching camera photosensors decreases with exposure times so that short-exposure recordings suffer from underexposure noise. In this paper, we propose to address this trade-off by treating the problem of high-speed imaging as an underexposed image denoising problem. We combine recent advances on underexposed image denoising using deep learning and adapt these methods to the specificity of the high-speed imaging problem. Leveraging large external datasets with a sensor-specific noise model, our method is able to speedup the acquisition rate of a High-Speed Camera over one order of magnitude while maintaining similar image quality.