Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding

• URL: http://arxiv.org/abs/2112.14768v1
• Date: Tue, 28 Dec 2021 02:06:44 GMT
• Title: Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding
• Authors: Erez Yosef, Shay Elmalem, Raja Giryes
• Abstract summary: We propose a hybrid optical-digital method for video reconstruction using a single motion-blurred image. We use a learned dynamic phase-coding in the lens aperture during the image acquisition to encode the motion trajectories. The proposed computational camera generates a sharp frame burst of the scene at various frame rates from a single coded motion-blurred image.
• Score: 34.76550131783525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video reconstruction from a single motion-blurred image is a challenging problem, which can enhance existing cameras' capabilities. 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.

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