Robust Single Image Deblurring Using Gyroscope Sensor

Seowon Ji, Jun Pyo Hong, Jeongmin Lee, Seung Jin Baek, Sung Jea Ko

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Motion blur in an image is caused by the movement of the camera during exposure time; thus, awareness of the camera motion is a key factor in image deblurring algorithms. Among the various sensors that can be utilized while taking a picture in handheld devices, a gyroscope sensor, which measures the angular velocity, can help in estimating the camera motion. To achieve accurate and efficient single-image deblurring with a gyroscope sensor, we present a novel deep network with a flexible receptive field that is appropriate for training features related to the nature of the blur. Two specialized modules are sequentially placed in the proposed network to adaptively convert the shapes of the convolutional kernels. The first module directly transforms the kernel shape into the direction of the camera motion indicated by the gyroscope measurements. In the middle of the network, where the feature abstraction is sufficiently proceeded, the second module integrates features from the blurry image along with the information from the gyroscope to convert the kernel shape effectively, even when the gyroscope sensor is unreliable. Using a new gyro-image paired dataset, extensive experiments were conducted to show the effects of the reliability of the gyroscope measurements on the deblurring performance and to prove the effectiveness of our strategy.

Original languageEnglish
Article number9444479
Pages (from-to)80835-80846
Number of pages12
JournalIEEE Access
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Convolutional neural network
  • gyroscope sensor
  • homography
  • single-image deblurring

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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