Gyroscope-Aided Motion Deblurring with Deep Networks

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.

Mustaniemi Janne, Kannala Juho, Särkkä Simo, Matas Jiri, Heikkilä Janne

A4 Article in conference proceedings

19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019

J. Mustaniemi, J. Kannala, S. Särkkä, J. Matas and J. Heikkila, "Gyroscope-Aided Motion Deblurring with Deep Networks," 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 2019, pp. 1914-1922. doi: 10.1109/WACV.2019.00208