# Exposure Trajectory Recovery from Motion Blur

arXiv: https://arxiv.org/abs/2010.02484 · HTML: https://mlsanity.com/paper/2010.02484/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 30 of 31)

- [Recent Advances for Quantum Neural Networks in Generative Learning](https://mlsanity.com/paper/2206.03066/) (2022)
- [Generalizing Event-Based Motion Deblurring in Real-World Scenarios](https://mlsanity.com/paper/2308.05932/) (2023)
- [Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time](https://mlsanity.com/paper/2303.15043/) (2023)
- [D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restoration](https://mlsanity.com/paper/2207.03294/) (2022)
- [Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks](https://mlsanity.com/paper/2308.02947/) (2023)
- [Dual-Camera Joint Deblurring-Denoising](https://mlsanity.com/paper/2309.08826/) (2023)
- [SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams](https://mlsanity.com/paper/2403.09486/) (2024)
- [Real-World Efficient Blind Motion Deblurring via Blur Pixel Discretization](https://mlsanity.com/paper/2404.12168/) (2024)
- [Deep learning in motion deblurring: current status, benchmarks and future prospects](https://mlsanity.com/paper/2401.05055/) (2024)
- [Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image](https://mlsanity.com/paper/2503.17358/) (2025)
- [Learning Parallax for Stereo Event-based Motion Deblurring](https://mlsanity.com/paper/2309.09513/) (2023)
- [CrossZoom: Simultaneously Motion Deblurring and Event Super-Resolving](https://mlsanity.com/paper/2309.16949/) (2023)
- [Generating the Past, Present and Future from a Motion-Blurred Image](https://mlsanity.com/paper/2512.19817/) (2025)
- [Neural Maximum A Posteriori Estimation on Unpaired Data for Motion Deblurring](https://mlsanity.com/paper/2204.12139/) (2022)
- [Deep Hybrid Camera Deblurring for Smartphone Cameras](https://mlsanity.com/paper/2312.13317/) (2023)
- ["It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models](https://mlsanity.com/paper/2511.08917/) (2025)
- [Recovering Continuous Scene Dynamics from A Single Blurry Image with Events](https://mlsanity.com/paper/2304.02695/) (2023)
- [Event-based Motion Deblurring via Multi-Temporal Granularity Fusion](https://mlsanity.com/paper/2412.11866/) (2024)
- [LVTINO: LAtent Video consisTency INverse sOlver for High Definition Video Restoration](https://mlsanity.com/paper/2510.01339/) (2025)
- [All-in-One Image Restoration via Causal-Deconfounding Wavelet-Disentangled Prompt Network](https://mlsanity.com/paper/2603.03839/) (2026)
- [Face Deblurring Based on Separable Normalization and Adaptive Denormalization](https://mlsanity.com/paper/2112.09833/) (2021)
- [Blur2seq: Blind Deblurring and Camera Trajectory Estimation from a Single Camera Motion-blurred Image](https://mlsanity.com/paper/2510.20539/) (2025)
- [Video shutter angle estimation using optical flow and linear blur](https://mlsanity.com/paper/2303.10247/) (2023)
- [Non-Uniform Exposure Imaging via Neuromorphic Shutter Control](https://mlsanity.com/paper/2404.13972/) (2024)
- [SpikeGen: Decoupled "Rods and Cones" Visual Representation Processing with Latent Generative Framework](https://mlsanity.com/paper/2505.18049/) (2025)
- [EVDI++: Event-based Video Deblurring and Interpolation via Self-Supervised Learning](https://mlsanity.com/paper/2509.08260/) (2025)
- [MoTDiff: High-resolution Motion Trajectory estimation from a single blurred image using Diffusion models](https://mlsanity.com/paper/2510.26173/) (2025)
- [Toward Generalizable Deblurring: Leveraging Massive Blur Priors with Linear Attention for Real-World Scenarios](https://mlsanity.com/paper/2601.06525/) (2026)
- [Dual-Exposure Imaging with Events](https://mlsanity.com/paper/2604.10273/) (2026)
- [AstraMoE-SR: Trajectory-Guided Diffusion for Blind Satellite Jitter Deblurring and Super-Resolution](https://mlsanity.com/paper/2609.07012/) (2026)

## Abstract (arXiv)

Motion blur in dynamic scenes is an important yet challenging research topic. Recently, deep learning methods have achieved impressive performance for dynamic scene deblurring. However, the motion information contained in a blurry image has yet to be fully explored and accurately formulated because: (i) the ground truth of dynamic motion is difficult to obtain; (ii) the temporal ordering is destroyed during the exposure; and (iii) the motion estimation from a blurry image is highly ill-posed. By revisiting the principle of camera exposure, motion blur can be described by the relative motions of sharp content with respect to each exposed position. In this paper, we define exposure trajectories, which represent the motion information contained in a blurry image and explain the causes of motion blur. A novel motion offset estimation framework is proposed to model pixel-wise displacements of the latent sharp image at multiple timepoints. Under mild constraints, our method can recover dense, (non-)linear exposure trajectories, which significantly reduce temporal disorder and ill-posed problems. Finally, experiments demonstrate that the recovered exposure trajectories not only capture accurate and interpretable motion information from a blurry image, but also benefit motion-aware image deblurring and warping-based video extraction tasks. Codes are available on https://github.com/yjzhang96/Motion-ETR.