Gyroscope-aided motion deblurring with deep networks

Mustaniemi, Janne; Kannala, Juho; Särkkä, Simo; Matas, Jiri; Heikkilä, Janne (2019-03-07)
 
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https://doi.org/10.1109/WACV.2019.00208

Mustaniemi, Janne
Kannala, Juho
Särkkä, Simo
Matas, Jiri
Heikkilä, Janne
Institute of Electrical and Electronics Engineers
07.03.2019

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

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doi:https://doi.org/10.1109/WACV.2019.00208
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https://urn.fi/URN:NBN:fi-fe2019060618814
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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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