# Light Field Blind Motion Deblurring

arXiv: https://arxiv.org/abs/1704.05416 · HTML: https://mlsanity.com/paper/1704.05416/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2017.

## Built on by (most cited first, 14 of 14)

- [DP-NeRF: Deblurred Neural Radiance Field with Physical Scene Priors](https://mlsanity.com/paper/2211.12046/) (2022)
- [ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images](https://mlsanity.com/paper/2309.08957/) (2023)
- [Real-World Light Field Image Super-Resolution via Degradation Modulation](https://mlsanity.com/paper/2206.06214/) (2022)
- [CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images](https://mlsanity.com/paper/2503.05332/) (2025)
- [SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields](https://mlsanity.com/paper/2403.07547/) (2024)
- [Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding](https://mlsanity.com/paper/2112.14768/) (2021)
- [GS-Blur: A 3D Scene-Based Dataset for Realistic Image Deblurring](https://mlsanity.com/paper/2410.23658/) (2024)
- [Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses](https://mlsanity.com/paper/2102.07085/) (2021)
- [Deep Idempotent Network for Efficient Single Image Blind Deblurring](https://mlsanity.com/paper/2210.07122/) (2022)
- [Joint 3D Shape and Motion Estimation from Rolling Shutter Light-Field Images](https://mlsanity.com/paper/2311.01292/) (2023)
- [CoCoGaussian: Leveraging Circle of Confusion for Gaussian Splatting from Defocused Images](https://mlsanity.com/paper/2412.16028/) (2024)
- [Human and Scene Motion Deblurring using Pseudo-blur Synthesizer](https://mlsanity.com/paper/2111.12911/) (2021)
- [Dynamic Gaussian Splatting from Defocused and Motion-blurred Monocular Videos](https://mlsanity.com/paper/2510.10691/) (2025)
- [View Adaptive Light Field Deblurring Networks with Depth Perception](https://mlsanity.com/paper/2303.06860/) (2023)

## Abstract (arXiv)

We study the problem of deblurring light fields of general 3D scenes captured under 3D camera motion and present both theoretical and practical contributions. By analyzing the motion-blurred light field in the primal and Fourier domains, we develop intuition into the effects of camera motion on the light field, show the advantages of capturing a 4D light field instead of a conventional 2D image for motion deblurring, and derive simple methods of motion deblurring in certain cases. We then present an algorithm to blindly deblur light fields of general scenes without any estimation of scene geometry, and demonstrate that we can recover both the sharp light field and the 3D camera motion path of real and synthetically-blurred light fields.