Gyroscope-aided motion deblurring with deep networks

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

doi:10.1109/WACV.2019.00208

Gyroscope-aided motion deblurring with deep networks

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

^*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

26 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
Kustantaja	IEEE
Sivut	1914-1922
Sivumäärä	9
ISBN (elektroninen)	9781728119755
DOI - pysyväislinkit	https://doi.org/10.1109/WACV.2019.00208
Tila	Julkaistu - 4 maalisk. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE Winter Conference on Applications of Computer Vision - Waikoloa Village, Yhdysvallat Kesto: 7 tammik. 2019 → 11 tammik. 2019 Konferenssinumero: 19

Julkaisusarja

Nimi	IEEE Winter Conference on Applications of Computer Vision
Kustantaja	IEEE
ISSN (painettu)	2472-6737

Conference

Conference	IEEE Winter Conference on Applications of Computer Vision
Lyhennettä	WACV
Maa/Alue	Yhdysvallat
Kaupunki	Waikoloa Village
Ajanjakso	07/01/2019 → 11/01/2019

Pääsy asiakirjaan

10.1109/WACV.2019.00208

https://arxiv.org/abs/1810.00986v2

Muut tiedostot ja linkit

Link to publication in Scopus

Siteeraa tätä

Mustaniemi, J., Kannala, J., Särkkä, S., Matas, J., & Heikkilä, J. (2019). Gyroscope-aided motion deblurring with deep networks. teoksessa Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019 (Sivut 1914-1922). Artikkeli 8658406 (IEEE Winter Conference on Applications of Computer Vision). IEEE. https://doi.org/10.1109/WACV.2019.00208

@inproceedings{aaf6d97d4a7e45a19e9b742f72f05937,

title = "Gyroscope-aided motion deblurring with deep networks",

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.",

keywords = "Cameras, Gyroscopes, Image motion analysis, Image restoration",

author = "Janne Mustaniemi and Juho Kannala and Simo S{\"a}rkk{\"a} and Jiri Matas and Janne Heikkil{\"a}",

year = "2019",

month = mar,

day = "4",

doi = "10.1109/WACV.2019.00208",

language = "English",

series = "IEEE Winter Conference on Applications of Computer Vision",

publisher = "IEEE",

pages = "1914--1922",

booktitle = "Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019",

address = "United States",

note = "IEEE Winter Conference on Applications of Computer Vision, WACV ; Conference date: 07-01-2019 Through 11-01-2019",

}

Mustaniemi, J, Kannala, J , Särkkä, S, Matas, J & Heikkilä, J 2019, Gyroscope-aided motion deblurring with deep networks. julkaisussa Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019., 8658406, IEEE Winter Conference on Applications of Computer Vision, IEEE, Sivut 1914-1922, IEEE Winter Conference on Applications of Computer Vision, Waikoloa Village, Hawaii, Yhdysvallat, 07/01/2019. https://doi.org/10.1109/WACV.2019.00208

Gyroscope-aided motion deblurring with deep networks. / Mustaniemi, Janne; Kannala, Juho ; Särkkä, Simo et al.
Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019. IEEE, 2019. s. 1914-1922 8658406 (IEEE Winter Conference on Applications of Computer Vision).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Gyroscope-aided motion deblurring with deep networks

AU - Mustaniemi, Janne

AU - Kannala, Juho

AU - Särkkä, Simo

AU - Matas, Jiri

AU - Heikkilä, Janne

N1 - Conference code: 19

PY - 2019/3/4

Y1 - 2019/3/4

N2 - 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.

AB - 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.

KW - Cameras

KW - Gyroscopes

KW - Image motion analysis

KW - Image restoration

UR - http://www.scopus.com/inward/record.url?scp=85063595270&partnerID=8YFLogxK

U2 - 10.1109/WACV.2019.00208

DO - 10.1109/WACV.2019.00208

M3 - Conference article in proceedings

AN - SCOPUS:85063595270

T3 - IEEE Winter Conference on Applications of Computer Vision

SP - 1914

EP - 1922

BT - Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019

PB - IEEE

T2 - IEEE Winter Conference on Applications of Computer Vision

Y2 - 7 January 2019 through 11 January 2019

ER -

Gyroscope-aided motion deblurring with deep networks

Abstrakti

Julkaisusarja

Conference

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Siteeraa tätä