# Learning Blind Motion Deblurring

arXiv: https://arxiv.org/abs/1708.04208 · HTML: https://mlsanity.com/paper/1708.04208/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2017.

## Built on by (most cited first, 30 of 36)

- [Deblurring via Stochastic Refinement](https://mlsanity.com/paper/2112.02475/) (2021)
- [Deblur-NeRF: Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2111.14292/) (2021)
- [Deep Image Deblurring: A Survey](https://mlsanity.com/paper/2201.10700/) (2022)
- [MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction](https://mlsanity.com/paper/2204.07908/) (2022)
- [Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2203.04845/) (2022)
- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [Flow-Guided Sparse Transformer for Video Deblurring](https://mlsanity.com/paper/2201.01893/) (2022)
- [ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images](https://mlsanity.com/paper/2309.08957/) (2023)
- [Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network](https://mlsanity.com/paper/2106.16028/) (2021)
- [Event-guided Deblurring of Unknown Exposure Time Videos](https://mlsanity.com/paper/2112.06988/) (2021)
- [Spatio-Temporal Deformable Attention Network for Video Deblurring](https://mlsanity.com/paper/2207.10852/) (2022)
- [On the Generalization of BasicVSR++ to Video Deblurring and Denoising](https://mlsanity.com/paper/2204.05308/) (2022)
- [Self-Supervised Image Restoration with Blurry and Noisy Pairs](https://mlsanity.com/paper/2211.07317/) (2022)
- [Blur-aware Spatio-temporal Sparse Transformer for Video Deblurring](https://mlsanity.com/paper/2406.07551/) (2024)
- [Hybrid Agents for Image Restoration](https://mlsanity.com/paper/2503.10120/) (2025)
- [Multi-Scale Memory-Based Video Deblurring](https://mlsanity.com/paper/2204.02977/) (2022)
- [Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance](https://mlsanity.com/paper/2207.10123/) (2022)
- [Deblurring Neural Radiance Fields with Event-driven Bundle Adjustment](https://mlsanity.com/paper/2406.14360/) (2024)
- [DeblurGS: Gaussian Splatting for Camera Motion Blur](https://mlsanity.com/paper/2404.11358/) (2024)
- [TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence](https://mlsanity.com/paper/2203.06764/) (2022)
- [Event-based Image Deblurring with Dynamic Motion Awareness](https://mlsanity.com/paper/2208.11398/) (2022)
- [EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration](https://mlsanity.com/paper/2105.04872/) (2021)
- [CMTA: Cross-Modal Temporal Alignment for Event-guided Video Deblurring](https://mlsanity.com/paper/2408.14930/) (2024)
- [E3NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2408.01840/) (2024)
- [Exploiting Deblurring Networks for Radiance Fields](https://mlsanity.com/paper/2502.14454/) (2025)
- [Learning to Deblur Polarized Images](https://mlsanity.com/paper/2402.18134/) (2024)
- [Progressive Deep Video Dehazing without Explicit Alignment Estimation](https://mlsanity.com/paper/2107.07837/) (2021)
- [Recurrence-in-Recurrence Networks for Video Deblurring](https://mlsanity.com/paper/2203.06418/) (2022)
- [Pay Attention to Hidden States for Video Deblurring: Ping-Pong Recurrent Neural Networks and Selective Non-Local Attention](https://mlsanity.com/paper/2203.16063/) (2022)
- [ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames and Events](https://mlsanity.com/paper/2409.14103/) (2024)

## Abstract (arXiv)

As handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime. However, taking a quick shot frequently yields a blurry result due to unwanted camera shake during recording or moving objects in the scene. Removing these artifacts from the blurry recordings is a highly ill-posed problem as neither the sharp image nor the motion blur kernel is known. Propagating information between multiple consecutive blurry observations can help restore the desired sharp image or video. Solutions for blind deconvolution based on neural networks rely on a massive amount of ground-truth data which is hard to acquire. In this work, we propose an efficient approach to produce a significant amount of realistic training data and introduce a novel recurrent network architecture to deblur frames taking temporal information into account, which can efficiently handle arbitrary spatial and temporal input sizes. We demonstrate the versatility of our approach in a comprehensive comparison on a number of challening real-world examples.