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.
@article{arxiv.1810.00986,
title = {Gyroscope-Aided Motion Deblurring with Deep Networks},
author = {Janne Mustaniemi and Juho Kannala and Simo Särkkä and Jiri Matas and Janne Heikkilä},
journal= {arXiv preprint arXiv:1810.00986},
year = {2018}
}