  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.
