Gyroscope-aided motion deblurring with deep networks

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PublisherIEEE
Pages1914-1922
Number of pages9
ISBN (Electronic)9781728119755
DOIs
Publication statusPublished - 4 Mar 2019
MoE publication typeA4 Article in a conference publication
EventIEEE Winter Conference on Applications of Computer Vision - Waikoloa Village, United States
Duration: 7 Jan 201911 Jan 2019
Conference number: 19

Publication series

NameIEEE Winter Conference on Applications of Computer Vision
PublisherIEEE
ISSN (Print)2472-6737

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision
Abbreviated titleWACV
CountryUnited States
CityWaikoloa Village
Period07/01/201911/01/2019

Keywords

  • Cameras
  • Gyroscopes
  • Image motion analysis
  • Image restoration

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