mlsanityData to 7 Oct 2026

Gyroscope-Aided Motion Deblurring with Deep Networks

arXiv 1810.00986 ↗First posted 1 Oct 2018cs.CVcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2018.

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Citing papers per year: 2021: 5; 2022: 1; 2023: 3; 2024: 3; 2025: 2; 2026: 1.

15 papers.

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