# Gyroscope-Aided Motion Deblurring with Deep Networks

arXiv: https://arxiv.org/abs/1810.00986 · HTML: https://mlsanity.com/paper/1810.00986/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2018.

## Built on by (most cited first, 15 of 15)

- [Deep Image Deblurring: A Survey](https://mlsanity.com/paper/2201.10700/) (2022)
- [MC-Blur: A Comprehensive Benchmark for Image Deblurring](https://mlsanity.com/paper/2112.00234/) (2021)
- [GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning](https://mlsanity.com/paper/2103.13725/) (2021)
- [GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning](https://mlsanity.com/paper/2301.10018/) (2023)
- [DeepOIS: Gyroscope-Guided Deep Optical Image Stabilizer Compensation](https://mlsanity.com/paper/2101.11183/) (2021)
- [Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding](https://mlsanity.com/paper/2105.13077/) (2021)
- [Single Image Rolling Shutter Removal with Diffusion Models](https://mlsanity.com/paper/2407.02906/) (2024)
- [Continuous Pose for Monocular Cameras in Neural Implicit Representation](https://mlsanity.com/paper/2311.17119/) (2023)
- [Gyro-based Neural Single Image Deblurring](https://mlsanity.com/paper/2404.00916/) (2024)
- [Fast and Accurate: Video Enhancement using Sparse Depth](https://mlsanity.com/paper/2103.08764/) (2021)
- [Depth-Aware Image Compositing Model for Parallax Camera Motion Blur](https://mlsanity.com/paper/2303.09334/) (2023)
- [Blur2seq: Blind Deblurring and Camera Trajectory Estimation from a Single Camera Motion-blurred Image](https://mlsanity.com/paper/2510.20539/) (2025)
- [Gyroscope-Assisted Motion Deblurring Network](https://mlsanity.com/paper/2402.06854/) (2024)
- [MoTDiff: High-resolution Motion Trajectory estimation from a single blurred image using Diffusion models](https://mlsanity.com/paper/2510.26173/) (2025)
- [RMR-P: Road Metadata-Aware Restoration for Pavement Inspection](https://mlsanity.com/paper/2608.08957/) (2026)

## 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.