Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding
Abstract
Video reconstruction from a single motion-blurred image is a challenging problem, which can enhance the capabilities of existing cameras. Recently, several works addressed this task using conventional imaging and deep learning. Yet, such purely-digital methods are inherently limited, due to direction ambiguity and noise sensitivity. Some works proposed to address these limitations using non-conventional image sensors, however, such sensors are extremely rare and expensive. To circumvent these limitations with simpler means, we propose a hybrid optical-digital method for video reconstruction that requires only simple modifications to existing optical systems. We use a learned dynamic phase-coding in the lens aperture during the image acquisition to encode the motion trajectories, which serve as prior information for the video reconstruction process. The proposed computational camera generates a sharp frame burst of the scene at various frame rates from a single coded motion-blurred image, using an image-to-video convolutional neural network. We present advantages and improved performance compared to existing methods, using both simulations and a real-world camera prototype. We extend our optical coding also to video frame interpolation and present robust and improved results for noisy videos.
Cite
@article{arxiv.2112.14768,
title = {Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding},
author = {Erez Yosef and Shay Elmalem and Raja Giryes},
journal= {arXiv preprint arXiv:2112.14768},
year = {2022}
}