Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements

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

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

8 Citations (Scopus)


Many computer vision and image processing applications rely on local features. It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors. We propose an inertial-based deblurring method for improving the robustness of existing feature detectors and descriptors against the motion blur. Unlike most deblurring algorithms, the method can handle spatially-variant blur and rolling shutter distortion. Furthermore, it is capable of running in real-time contrary to state-of-the-art algorithms. The limitations of inertial-based blur estimation are taken into account by validating the blur estimates using image data. The evaluation shows that when the method is used with traditional feature detector and descriptor, it increases the number of detected keypoints, provides higher repeatability and improves the localization accuracy. We also demonstrate that such features will lead to more accurate and complete reconstructions when used in the application of 3D visual reconstruction.
Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition (ICPR)
Pages3068 -3073
ISBN (Electronic)978-1-5386-3788-3
ISBN (Print)978-1-5386-3787-6
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Pattern Recognition - Beijing, China
Duration: 20 Aug 201824 Aug 2018
Conference number: 24

Publication series

NameInternational Conference on Pattern Recognition (ICPR)
ISSN (Print)1051-4651


ConferenceInternational Conference on Pattern Recognition
Abbreviated titleICPR


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