# Affine-modeled video extraction from a single motion blurred image

arXiv: https://arxiv.org/abs/2104.03777 · HTML: https://mlsanity.com/paper/2104.03777/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 23 of 23)

- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [Generating Videos with Scene Dynamics](https://mlsanity.com/paper/1609.02612/) (2016)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017)
- [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://mlsanity.com/paper/1908.03826/) (2019)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015)
- [Unsupervised Learning for Physical Interaction through Video Prediction](https://mlsanity.com/paper/1605.07157/) (2016)
- [Temporal Generative Adversarial Nets with Singular Value Clipping](https://mlsanity.com/paper/1611.06624/) (2016)
- [Video Frame Interpolation via Adaptive Convolution](https://mlsanity.com/paper/1703.07514/) (2017)
- [Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal](https://mlsanity.com/paper/1503.00593/) (2015)
- [MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement](https://mlsanity.com/paper/1810.08768/) (2018)
- [Adversarial Spatio-Temporal Learning for Video Deblurring](https://mlsanity.com/paper/1804.00533/) (2018)
- [Learning to Extract a Video Sequence from a Single Motion-Blurred Image](https://mlsanity.com/paper/1804.04065/) (2018)
- [Online Video Deblurring via Dynamic Temporal Blending Network](https://mlsanity.com/paper/1704.03285/) (2017)
- [Predicting Deeper into the Future of Semantic Segmentation](https://mlsanity.com/paper/1703.07684/) (2017)
- [Bringing Alive Blurred Moments](https://mlsanity.com/paper/1804.02913/) (2018)
- [Spatio-temporal video autoencoder with differentiable memory](https://mlsanity.com/paper/1511.06309/) (2015)
- [Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel](https://mlsanity.com/paper/1708.03423/) (2017)
- [Learning a Discriminative Prior for Blind Image Deblurring](https://mlsanity.com/paper/1803.03363/) (2018)
- [Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions](https://mlsanity.com/paper/1909.00475/) (2019)
- [Bi-l0-l2-Norm Regularization for Blind Motion Deblurring](https://mlsanity.com/paper/1408.4712/) (2014)

## Built on by (most cited first, 1 of 1)

- [Generating the Past, Present and Future from a Motion-Blurred Image](https://mlsanity.com/paper/2512.19817/) (2025)

## Abstract (arXiv)

A motion-blurred image is the temporal average of multiple sharp frames over the exposure time. Recovering these sharp video frames from a single blurred image is nontrivial, due to not only its strong ill-posedness, but also various types of complex motion in reality such as rotation and motion in depth. In this work, we report a generalized video extraction method using the affine motion modeling, enabling to tackle multiple types of complex motion and their mixing. In its workflow, the moving objects are first segemented in the alpha channel. This allows separate recovery of different objects with different motion. Then, we reduce the variable space by modeling each video clip as a series of affine transformations of a reference frame, and introduce the l0-norm total variation regularization to attenuate the ringing artifact. The differentiable affine operators are employed to realize gradient-descent optimization of the affine model, which follows a novel coarse-to-fine strategy to further reduce artifacts. As a result, both the affine parameters and sharp reference image are retrieved. They are finally input into stepwise affine transformation to recover the sharp video frames. The stepwise retrieval maintains the nature to bypass the frame order ambiguity. Experiments on both public datasets and real captured data validate the state-of-the-art performance of the reported technique.