# Multi-Scale Memory-Based Video Deblurring

arXiv: https://arxiv.org/abs/2204.02977 · HTML: https://mlsanity.com/paper/2204.02977/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 36 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [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)
- [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018)
- [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation](https://mlsanity.com/paper/2106.05210/) (2021)
- [Frame-Recurrent Video Super-Resolution](https://mlsanity.com/paper/1801.04590/) (2018)
- [Video Object Segmentation with Episodic Graph Memory Networks](https://mlsanity.com/paper/2007.07020/) (2020)
- [Kernelized Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2007.08270/) (2020)
- [Spatio-Temporal Filter Adaptive Network for Video Deblurring](https://mlsanity.com/paper/1904.12257/) (2019)
- [Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization](https://mlsanity.com/paper/2002.11244/) (2020)
- [Cascaded Deep Video Deblurring Using Temporal Sharpness Prior](https://mlsanity.com/paper/2004.02501/) (2020)
- [Learning Dynamic Memory Networks for Object Tracking](https://mlsanity.com/paper/1803.07268/) (2018)
- [Online Video Deblurring via Dynamic Temporal Blending Network](https://mlsanity.com/paper/1704.03285/) (2017)
- [Learning Blind Motion Deblurring](https://mlsanity.com/paper/1708.04208/) (2017)
- [A Read-Write Memory Network for Movie Story Understanding](https://mlsanity.com/paper/1709.09345/) (2017)
- [Deep Video Deblurring: The Devil is in the Details](https://mlsanity.com/paper/1909.12196/) (2019)

## Built on by (most cited first, 16 of 16)

- [Imaging through the Atmosphere using Turbulence Mitigation Transformer](https://mlsanity.com/paper/2207.06465/) (2022)
- [Blur-aware Spatio-temporal Sparse Transformer for Video Deblurring](https://mlsanity.com/paper/2406.07551/) (2024)
- [Domain-adaptive Video Deblurring via Test-time Blurring](https://mlsanity.com/paper/2407.09059/) (2024)
- [Learning to Deblur Polarized Images](https://mlsanity.com/paper/2402.18134/) (2024)
- [Instruct-ReID++: Towards Universal Purpose Instruction-Guided Person Re-identification](https://mlsanity.com/paper/2405.17790/) (2024)
- [Reference-based Restoration of Digitized Analog Videotapes](https://mlsanity.com/paper/2310.14926/) (2023)
- [Quanta Video Restoration](https://mlsanity.com/paper/2410.14994/) (2024)
- [VJT: A Video Transformer on Joint Tasks of Deblurring, Low-light Enhancement and Denoising](https://mlsanity.com/paper/2401.14754/) (2024)
- [UniFlowRestore: A General Video Restoration Framework via Flow Matching and Prompt Guidance](https://mlsanity.com/paper/2504.09069/) (2025)
- [Coding-Prior Guided Diffusion Network for Video Deblurring](https://mlsanity.com/paper/2504.12222/) (2025)
- [Learning to Pan-sharpening with Memories of Spatial Details](https://mlsanity.com/paper/2306.16181/) (2023)
- [ViStripformer: A Token-Efficient Transformer for Versatile Video Restoration](https://mlsanity.com/paper/2312.14502/) (2023)
- [Adaptive High-Pass Kernel Prediction for Efficient Video Deblurring](https://mlsanity.com/paper/2412.01559/) (2024)
- [Joint Depth and Reflectivity Estimation using Single-Photon LiDAR](https://mlsanity.com/paper/2505.13250/) (2025)
- [Learning Event-guided Exposure-agnostic Video Frame Interpolation via Adaptive Feature Blending](https://mlsanity.com/paper/2510.22565/) (2025)
- [Wavelet-Domain Masked Image Modeling for Color-Consistent HDR Video Reconstruction](https://mlsanity.com/paper/2602.07393/) (2026)

## Abstract (arXiv)

Video deblurring has achieved remarkable progress thanks to the success of deep neural networks. Most methods solve for the deblurring end-to-end with limited information propagation from the video sequence. However, different frame regions exhibit different characteristics and should be provided with corresponding relevant information. To achieve fine-grained deblurring, we designed a memory branch to memorize the blurry-sharp feature pairs in the memory bank, thus providing useful information for the blurry query input. To enrich the memory of our memory bank, we further designed a bidirectional recurrency and multi-scale strategy based on the memory bank. Experimental results demonstrate that our model outperforms other state-of-the-art methods while keeping the model complexity and inference time low. The code is available at https://github.com/jibo27/MemDeblur.