Awesome
Deep Learning in Motion Deblurring: Current Status, Benchmarks and Future Prospects 
:fire::fire: In this review, we have systematically examined over 150 papers :page_with_curl::page_with_curl::page_with_curl:, summarizing and analyzing :star2:more than 30 blind motion deblurring methods.
:fire::fire::fire: Extensive qualitative and quantitative comparisons have been conducted against the current SOTA methods on four datasets, highlighting their limitations and pointing out future research directions.
:fire::fire::fire::fire: The latest deblurring papers of CVPR 2024 have been included~
Fig 1. Overview of deep learning methods for blind motion deblurring.
Content:
- <a href="#survey"> Related Reviews and Surveys to Deblurring </a>
- <a href="#cnnmodels"> CNN-based Blind Motion Deblurring Models </a>
- <a href="#rnnmodels"> RNN-based Blind Motion Deblurring Models </a>
- <a href="#ganmodels"> GAN-based Blind Motion Deblurring Models </a>
- <a href="#tmodels"> Transformer-based Blind Motion Deblurring Models </a>
- <a href="#diffmodels"> Diffusion-based Blind Motion Deblurring Models </a>
- <a href="#datasets"> Motion Deblurring Datasets </a>
- <a href="#evaluation"> Evaluation </a>
- <a href="#citation"> Citation </a>
0. Related Reviews and Surveys to Deblurring: <a id="survey" class="anchor" href="#survey" aria-hidden="true"><span class="octicon octicon-link"></span></a>
:rocket::rocket::rocket:Update (in 2023-12-28) :balloon:
| No. | Year | Pub. | Title | Links |
|---|---|---|---|---|
| 01 | 2021 | CDS | A Survey on Single Image Deblurring | Paper/Project |
| 02 | 2021 | CVIU | Single-image deblurring with neural networks: A comparative survey | Paper/Project |
| 03 | 2022 | IJCV | Deep Image Deblurring: A Survey | Paper/Project |
| 04 | 2022 | arXiv | Blind Image Deblurring: A Review | Paper/Project |
| 05 | 2023 | CVMJ | A survey on facial image deblurring | Paper/Project |
| 06 | 2023 | arXiv | A Comprehensive Survey on Deep Neural Image Deblurring | Paper/Project |
1. CNN-based Blind Motion Deblurring Models: <a id="cnnmodels" class="anchor" href="#CNNmodels" aria-hidden="true"><span class="octicon octicon-link"></span></a>
:rocket::rocket::rocket:Update (in 2024-05-14) :balloon:
| No. | Year | Model | Pub. | Title | Links |
|---|---|---|---|---|---|
| 01 | 2017 | DeepDeblur | CVPR | Deep multi-scale convolutional neural network for dynamic scene deblurring | Paper/Project |
| 02 | 2019 | DMPHN | CVPR | Deep stacked hierarchical multi-patch network for image deblurring | Paper/Project |
| 03 | 2019 | PSS-NSC | CVPR | Dynamic scene deblurring with parameter selective sharing and nested skip connections | Paper/Project |
| 04 | 2020 | DGN | TIP | Dynamic scene deblurring by depth guided model | Paper/Project |
| 05 | 2020 | MSCAN | TCSVT | Deep convolutional-neural-network-based channel attention for single image dynamic scene blind deblurring | Paper/Project |
| 06 | 2021 | SDWNet | ICCVW | Sdwnet: A straight dilated network with wavelet transformation for image deblurring | Paper/Project |
| 07 | 2021 | TIP | Deep Outlier Handling for Image Deblurring | Paper/[Project] | |
| 08 | 2021 | MIMOU-Net+ | ICCV | Rethinking coarse-to-fine approach in single image deblurring | Paper/Project |
| 09 | 2021 | MPRNet | CVPR | Multi-stage progressive image restoration | Paper/Project |
| 10 | 2022 | MSSNet | ECCVW | Mssnet: Multi-scale-stage network for single image deblurring | Paper/Project |
| 11 | 2022 | HINet | CVPRW | Hinet: Half instance normalization network for image restoration | Paper/Project |
| 12 | 2022 | BANet | TIP | Banet: a blur-aware attention network for dynamic scene deblurring | Paper/Project |
| 13 | 2022 | IRNeXt | ICML | Irnext: Rethinking convolutional network design for image restoration | Paper/Project |
| 14 | 2023 | ReLoBlur | AAAI | Real-World Deep Local Motion Deblurring | Paper/Project |
| 15 | 2023 | MRLPFNet | ICCV | Multi-scale Residual Low-Pass Filter Network for Image Deblurring | Paper/[Project] |
| 16 | 2023 | MSFS-FNet | TCSVT | Multi-Scale Frequency Separation Network for Image Deblurring | Paper/Project |
2. RNN-based Blind Motion Deblurring Models: <a id="rnnmodels" class="anchor" href="#RNNmodels" aria-hidden="true"><span class="octicon octicon-link"></span></a>
:rocket::rocket::rocket:Update (in 2024-05-14) :balloon:
| No. | Year | Model | Pub. | Title | Links |
|---|---|---|---|---|---|
| 01 | 2018 | SVRNN | CVPR | Dynamic scene deblurring using spatially variant recurrent neural networks | Paper/Project |
| 02 | 2018 | SRN | CVPR | Scale-recurrent network for deep image deblurring | Paper/Project |
| 03 | 2022 | TCSVT | Deep Dynamic Scene Deblurring From Optical Flow | Paper/[Project] | |
| 04 | 2023 | MT-RNN | ECCV | Multi-temporal recurrent neural networks for progressive non-uniform single image deblurring with incremental temporal training | Paper/Project |
3. GAN-based Blind Motion Deblurring Models: <a id="ganmodels" class="anchor" href="#GANmodels" aria-hidden="true"><span class="octicon octicon-link"></span></a>
:rocket::rocket::rocket:Update (in 2024-05-14) :balloon:
| No. | Year | Model | Pub. | Title | Links |
|---|---|---|---|---|---|
| 01 | 2018 | DeblurGAN | CVPR | Deblurgan: Blind motion deblurring using conditional adversarial networks | Paper/Project |
| 02 | 2019 | DeblurGAN-V2 | ICCV | Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better | Paper/Project |
| 03 | 2020 | DBGAN | CVPR | Distribution-induced Bidirectional GAN for Graph Representation Learning | Paper/Project |
| 04 | 2021 | CycleGAN | ICCV | Unpaired image-to-image translation using cycle-consistent adversarial networks | Paper/Project |
| 05 | 2021 | TPAMI | Physics-Based Generative Adversarial Models for Image Restoration and Beyond | Paper/[Project] | |
| 06 | 2022 | FCLGAN | ACM | Unpaired image-to-image translation using cycle-consistent adversarial networks | Paper/Project |
| 07 | 2022 | Ghost-DeblurGAN | IROS | Application of Ghost-DeblurGAN to Fiducial Marker Detection | Paper/Project |
4. Transformer-based Blind Motion Deblurring Models: <a id="tmodels" class="anchor" href="#Tmodels" aria-hidden="true"><span class="octicon octicon-link"></span></a>
:rocket::rocket::rocket:Update (in 2024-05-14) :balloon:
| No. | Year | Model | Pub. | Title | Links |
|---|---|---|---|---|---|
| 01 | 2021 | Uformer | CVPR | Uformer: A general u-shaped transformer for image restoration | Paper/Project |
| 02 | 2022 | Restormer | CVPR | Restormer: Efficient transformer for high-resolution image restoration | Paper/Project |
| 03 | 2022 | Stripformer | ECCV | Stripformer: Strip transformer for fast image deblurring | Paper/Project |
| 04 | 2022 | Stoformer | NeurIPS | Stochastic Window Transformer for Image Restoration | Paper/Project |
| 05 | 2023 | Sharpformer | TIP | SharpFormer: Learning Local Feature Preserving Global Representations for Image Deblurring | Paper/Project |
| 06 | 2023 | FFTformer | CVPR | Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring | Paper/Project |
| 07 | 2023 | BiT | CVPR | Blur Interpolation Transformer for Real-World Motion from Blur | Paper/Project |
| 08 | 2024 | CVPR | Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Debluring | [Paper]/[Project] | |
| 09 | 2024 | TNNLS | Image Deblurring by Exploring In-Depth Properties of Transformer | [Paper]/Project |
5. Diffusion-based Blind Motion Deblurring Models: <a id="diffmodels" class="anchor" href="#Diffmodels" aria-hidden="true"><span class="octicon octicon-link"></span></a>
:rocket::rocket::rocket:Update (in 2024-05-14) :balloon:
| No. | Year | Model | Pub. | Title | Links |
|---|---|---|---|---|---|
| 01 | 2023 | ICCV | Multiscale Structure Guided Diffusion for Image Deblurring | Paper/[Project] | |
| 02 | 2024 | ID-Blau | CVPR | ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation | Paper/[Project] |
| 03 | 2024 | CVPR | Fourier Priors-Guided Diffusion for Zero-Shot Joint Low-Light Enhancement and Deblurring | [Paper]/[Project] |
6. Motion Deblurring Datasets: <a id="datasets" class="anchor" href="#datasets" aria-hidden="true"><span class="octicon octicon-link"></span></a>
:rocket::rocket::rocket:Update (in 2024-01-08) :balloon:
| No. | Dataset | Year | Pub. | Size | Types | Train/Val/Test | Download |
|---|---|---|---|---|---|---|---|
| 01 | Köhler at al. | 2012 | ECCV | 4 sharp, 48 blur | Synthetic | - | link |
| 02 | GoPro | 2017 | CVPR | 3214 | Synthetic | 2103/0/1111 | link |
| 03 | HIDE | 2019 | CVPR | 8422 | Synthetic | 6397/0/2025 | link |
| 04 | Blur-DVS | 2020 | CVPR | 13358 | Real | 8878/1120/3360 | [link] |
| 05 | RealBlur | 2020 | ECCV | 4738 | Real | 3758/0/980 | link |
| 06 | RsBlur | 2022 | ECCV | 13358 | Real | 8878/1120/3360 | link |
| 07 | ReLoBlur | 2023 | AAAI | 2405 | Real | 2010/0/395 | link |
7. Evaluation: <a id="evaluation" class="anchor" href="#evaluation" aria-hidden="true"><span class="octicon octicon-link"></span></a>
- For evaluation on GoPro results in MATLAB, modify './out/...' to the corresponding path
evaluation_GoPro.m
- For evaluation on HIDE results in MATLAB, modify './out/...' to the corresponding path
evaluation_HIDE.m
- For evaluation on RealBlur_J results, modify './out/...' to the corresponding path
python evaluate_RealBlur_J.py
- For evaluation on RealBlur_R results, modify './out/...' to the corresponding path
python evaluate_RealBlur_R.py
Citation: <a id="citation" class="anchor" href="#citation" aria-hidden="true"><span class="octicon octicon-link"></span></a>
If you find our survey paper and evaluation code are useful, please cite the following paper:
@article{xiang2024deep,
title={Deep learning in motion deblurring: current status, benchmarks and future prospects},
author={Xiang, Yawen and Zhou, Heng and Li, Chengyang and Sun, Fangwei and Li, Zhongbo and Xie, Yongqiang},
journal={The Visual Computer},
pages={1--27},
year={2024},
publisher={Springer}
}