# Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding

arXiv: https://arxiv.org/abs/2112.14768 · HTML: https://mlsanity.com/paper/2112.14768/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 80 references that are in the map.
- First posted on arXiv 28 Dec 2021.

## Builds on (most cited first, 30 of 36)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017)
- [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018)
- [Depth-Aware Video Frame Interpolation](https://mlsanity.com/paper/1904.00830/) (2019)
- [Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera](https://mlsanity.com/paper/1811.10180/) (2018)
- [XVFI: eXtreme Video Frame Interpolation](https://mlsanity.com/paper/2103.16206/) (2021)
- [Deep Optics for Monocular Depth Estimation and 3D Object Detection](https://mlsanity.com/paper/1904.08601/) (2019)
- [Deep Optics for Single-shot High-dynamic-range Imaging](https://mlsanity.com/paper/1908.00620/) (2019)
- [Coded aperture compressive temporal imaging](https://mlsanity.com/paper/1302.2575/) (2013)
- [Learning to Extract a Video Sequence from a Single Motion-Blurred Image](https://mlsanity.com/paper/1804.04065/) (2018)
- [Blurry Video Frame Interpolation](https://mlsanity.com/paper/2002.12259/) (2020)
- [Bringing Alive Blurred Moments](https://mlsanity.com/paper/1804.02913/) (2018)
- [Video Frame Interpolation without Temporal Priors](https://mlsanity.com/paper/2112.01161/) (2021)
- [Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging](https://mlsanity.com/paper/1808.03571/) (2018)
- [Miniscope3D: optimized single-shot miniature 3D fluorescence microscopy](https://mlsanity.com/paper/2010.05382/) (2020)
- [Light Field Blind Motion Deblurring](https://mlsanity.com/paper/1704.05416/) (2017)
- [Video from Stills: Lensless Imaging with Rolling Shutter](https://mlsanity.com/paper/1905.13221/) (2019)
- [Motion-blurred Video Interpolation and Extrapolation](https://mlsanity.com/paper/2103.02984/) (2021)
- [CodedStereo: Learned Phase Masks for Large Depth-of-field Stereo](https://mlsanity.com/paper/2104.04641/) (2021)
- [Photosequencing of Motion Blur using Short and Long Exposures](https://mlsanity.com/paper/1912.06102/) (2019)

## Built on by (most cited first, 5 of 5)

- [Learning Spatially Varying Pixel Exposures for Motion Deblurring](https://mlsanity.com/paper/2204.07267/) (2022)
- [Zero-Shot Personalized Camera Motion Control for Image-to-Video Synthesis](https://mlsanity.com/paper/2504.09472/) (2025)
- [Lightweight High-Speed Photography Built on Coded Exposure and Implicit Neural Representation of Videos](https://mlsanity.com/paper/2311.13134/) (2023)
- [Deep Phase Coded Image Prior](https://mlsanity.com/paper/2404.03906/) (2024)
- [ChronoSC: Task-Oriented Semantic Communication via Temporal-to-Color Encoding](https://mlsanity.com/paper/2605.16388/) (2026)

## Abstract (arXiv)

Video reconstruction from a single motion-blurred image is a challenging problem, which can enhance the capabilities of existing cameras. Recently, several works addressed this task using conventional imaging and deep learning. Yet, such purely-digital methods are inherently limited, due to direction ambiguity and noise sensitivity. Some works proposed to address these limitations using non-conventional image sensors, however, such sensors are extremely rare and expensive. To circumvent these limitations with simpler means, we propose a hybrid optical-digital method for video reconstruction that requires only simple modifications to existing optical systems. We use a learned dynamic phase-coding in the lens aperture during the image acquisition to encode the motion trajectories, which serve as prior information for the video reconstruction process. The proposed computational camera generates a sharp frame burst of the scene at various frame rates from a single coded motion-blurred image, using an image-to-video convolutional neural network. We present advantages and improved performance compared to existing methods, using both simulations and a real-world camera prototype. We extend our optical coding also to video frame interpolation and present robust and improved results for noisy videos.