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arXiv · 1810.00986

Gyroscope-Aided Motion Deblurring with Deep Networks

Janne Mustaniemi·Juho Kannala·Simo Särkkä·Jiri Matas·Janne Heikkilä
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Abstract

We propose a deblurring method that incorporates gyroscope measurements into a convolutional neural network (CNN). With the help of such measurements, it can handle extremely strong and spatially-variant motion blur. At the same time, the image data is used to overcome the limitations of gyro-based blur estimation. To train our network, we also introduce a novel way of generating realistic training data using the gyroscope. The evaluation shows a clear improvement in visual quality over the state-of-the-art while achieving real-time performance. Furthermore, the method is shown to improve the performance of existing feature detectors and descriptors against the motion blur.

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Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä. 2018-10-01. Gyroscope-Aided Motion Deblurring with Deep Networks. https://arxiv.org/abs/1810.00986

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