Image Deblurring Using Smartphone Inertial Sensors

Zhe Hu, Lu Yuan, Stephen Lin, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)


Removing image blur caused by camera shake is an ill-posed problem, as both the latent image and the point spread function (PSF) are unknown. A recent approach to address this problem is to record camera motion through inertial sensors, i.e., gyroscopes and accelerometers, and then reconstruct spatially-variant PSFs from these readings. While this approach has been effective for highquality inertial sensors, it has been infeasible for the inertial sensors in smartphones, which are of relatively low quality and present a number of challenging issues, including varying sensor parameters, high sensor noise, and calibration error. In this paper, we identify the issues that plague smartphone inertial sensors and propose a solution that successfully utilizes the sensor readings for image deblurring. With both the sensor data and the image itself, the proposed method is able to accurately estimate the sensor parameters online and also the spatially-variant PSFs for enhanced deblurring performance. The effectiveness of this technique is demonstrated in experiments on a popular mobile phone. With this approach, the quality of image deblurring can be appreciably raised on the most common of imaging devices.

Original languageEnglish
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
PublisherIEEE Computer Society
Number of pages10
ISBN (Electronic)9781467388504
Publication statusPublished - 2016 Dec 9
Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States
Duration: 2016 Jun 262016 Jul 1

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919


Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
Country/TerritoryUnited States
CityLas Vegas

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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