# Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution from a Blurred Image Sequence

arXiv: https://arxiv.org/abs/1709.05745 · HTML: https://mlsanity.com/paper/1709.05745/

## Key facts

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

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

- [Deblur-NeRF: Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2111.14292/) (2021)
- [BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields](https://mlsanity.com/paper/2211.12853/) (2022)
- [BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting](https://mlsanity.com/paper/2403.11831/) (2024)
- [ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images](https://mlsanity.com/paper/2309.08957/) (2023)
- [DeblurGS: Gaussian Splatting for Camera Motion Blur](https://mlsanity.com/paper/2404.11358/) (2024)
- [USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields](https://mlsanity.com/paper/2310.02687/) (2023)
- [Restoration of Video Frames from a Single Blurred Image with Motion Understanding](https://mlsanity.com/paper/2104.09134/) (2021)
- [Learning to Super-Resolve Blurry Images with Events](https://mlsanity.com/paper/2302.13766/) (2023)
- [Deblur e-NeRF: NeRF from Motion-Blurred Events under High-speed or Low-light Conditions](https://mlsanity.com/paper/2409.17988/) (2024)
- [Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image](https://mlsanity.com/paper/2503.17358/) (2025)
- [DAVIDE: Depth-Aware Video Deblurring](https://mlsanity.com/paper/2409.01274/) (2024)
- [Depth-Aware Image Compositing Model for Parallax Camera Motion Blur](https://mlsanity.com/paper/2303.09334/) (2023)
- [Super-Resolving Blurry Images with Events](https://mlsanity.com/paper/2405.06918/) (2024)
- [Is-NeRF: In-scattering Neural Radiance Field for Blurred Images](https://mlsanity.com/paper/2508.13808/) (2025)

## Abstract (arXiv)

The conventional methods for estimating camera poses and scene structures from severely blurry or low resolution images often result in failure. The off-the-shelf deblurring or super-resolution methods may show visually pleasing results. However, applying each technique independently before matching is generally unprofitable because this naive series of procedures ignores the consistency between images. In this paper, we propose a pioneering unified framework that solves four problems simultaneously, namely, dense depth reconstruction, camera pose estimation, super-resolution, and deblurring. By reflecting a physical imaging process, we formulate a cost minimization problem and solve it using an alternating optimization technique. The experimental results on both synthetic and real videos show high-quality depth maps derived from severely degraded images that contrast the failures of naive multi-view stereo methods. Our proposed method also produces outstanding deblurred and super-resolved images unlike the independent application or combination of conventional video deblurring, super-resolution methods.