# Stereo Video Deblurring

arXiv: https://arxiv.org/abs/1607.08421 · HTML: https://mlsanity.com/paper/1607.08421/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2016.

## Built on by (most cited first, 8 of 8)

- [Deep Image Deblurring: A Survey](https://mlsanity.com/paper/2201.10700/) (2022)
- [Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring](https://mlsanity.com/paper/2302.07689/) (2023)
- [Restoration of Video Frames from a Single Blurred Image with Motion Understanding](https://mlsanity.com/paper/2104.09134/) (2021)
- [Deep Dynamic Scene Deblurring from Optical Flow](https://mlsanity.com/paper/2301.07329/) (2023)
- [Learning Parallax for Stereo Event-based Motion Deblurring](https://mlsanity.com/paper/2309.09513/) (2023)
- [Human and Scene Motion Deblurring using Pseudo-blur Synthesizer](https://mlsanity.com/paper/2111.12911/) (2021)
- [MatRes: Zero-Shot Test-Time Model Adaptation for Simultaneous Matching and Restoration](https://mlsanity.com/paper/2604.10081/) (2026)
- [A Benchmark for Heterogeneous Stereo Deblurring with Physically- and Epipolar-constrained Cross Attention](https://mlsanity.com/paper/2606.25962/) (2026)

## Abstract (arXiv)

Videos acquired in low-light conditions often exhibit motion blur, which depends on the motion of the objects relative to the camera. This is not only visually unpleasing, but can hamper further processing. With this paper we are the first to show how the availability of stereo video can aid the challenging video deblurring task. We leverage 3D scene flow, which can be estimated robustly even under adverse conditions. We go beyond simply determining the object motion in two ways: First, we show how a piecewise rigid 3D scene flow representation allows to induce accurate blur kernels via local homographies. Second, we exploit the estimated motion boundaries of the 3D scene flow to mitigate ringing artifacts using an iterative weighting scheme. Being aware of 3D object motion, our approach can deal robustly with an arbitrary number of independently moving objects. We demonstrate its benefit over state-of-the-art video deblurring using quantitative and qualitative experiments on rendered scenes and real videos.