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https://github.com/kawchar85/Awesome-Deblurring-Resources

A curated list of research papers and datasets related to image and video deblurring.
https://github.com/kawchar85/Awesome-Deblurring-Resources
List: Awesome-Deblurring-Resources
awesome-list camera-shake collections computer-vision deblur deblurring defocus-blur defocus-deblurring image image-deconvolution image-restoration machine-learning motion-deblurring out-of-focus restoration video-deblurring
Last synced: 3 months ago
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A curated list of research papers and datasets related to image and video deblurring.
Host: GitHub
URL: https://github.com/kawchar85/Awesome-Deblurring-Resources
Owner: kawchar85
License: mit
Created: 2024-08-25T19:21:17.000Z (4 months ago)
Default Branch: main
Last Pushed: 2024-08-30T19:00:00.000Z (4 months ago)
Last Synced: 2024-09-05T00:01:32.482Z (4 months ago)
Topics: awesome-list, camera-shake, collections, computer-vision, deblur, deblurring, defocus-blur, defocus-deblurring, image, image-deconvolution, image-restoration, machine-learning, motion-deblurring, out-of-focus, restoration, video-deblurring
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Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README

        # Awesome-Deblurring-Resources

A curated list of research papers and datasets related to image and video deblurring.

## Overview

- [2024 Papers](#2024-papers)

- [2023 Papers](#2023-papers)

- [2022 Papers](#2022-papers)

- [2021 Papers](#2021-papers)

- [2020 Papers](#2020-papers)

- [2019 Papers](#2019-papers)

- [Datasets](#datasets)

## 2024 Papers

| Venue | Paper | Link |

|-------|-------|------|

| arxiv | [Blind Image Deblurring using FFT-ReLU with Deep Learning Pipeline Integration](https://arxiv.org/abs/2406.08344v1) | [Code](https://github.com/Metalicana/Blind-Image-Deblurring-using-FFT-ReLU-with-Deep-Learning-Pipeline-Integration) |

| arxiv | [Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution](https://arxiv.org/abs/2309.00287v2) | [FastDiffusionEM](https://github.com/claroche-r/fastdiffusionem) |

| SPIE | [Estimation of motion blur kernel parameters using regression convolutional neural networks](https://arxiv.org/abs/2308.01381v3) | [RegressionBlur](https://github.com/duckduckpig/regression_blur) |

| CVPR | [A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning](https://arxiv.org/abs/2403.02611) | [MPT-CataBlur](https://github.com/PieceZhang/MPT-CataBlur) |

| CVPR | [AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring](https://arxiv.org/abs/2406.09135) | [AdaRevD](https://github.com/INVOKERer/AdaRevD) |

| CVPR | [Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains](https://arxiv.org/abs/2403.16205) | [Blur2Blur](https://github.com/VinAIResearch/Blur2Blur) |

| CVPR | [Fourier Priors-Guided Diffusion for Zero-Shot Joint Low-Light Enhancement and Deblurring](https://openaccess.thecvf.com/content/CVPR2024/html/Lv_Fourier_Priors-Guided_Diffusion_for_Zero-Shot_Joint_Low-Light_Enhancement_and_Deblurring_CVPR_2024_paper.html) | [FourierDiff](https://github.com/aipixel/FourierDiff) |

| CVPR | [ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation](https://arxiv.org/abs/2312.10998) | [ID-Blau](https://github.com/plusgood-steven/ID-Blau) |

| CVPR | [LDP: Language-driven Dual-Pixel Image Defocus Deblurring Network](https://arxiv.org/abs/2307.09815) | [LDP](https://github.com/noxsine/LDP) |

| CVPR | [Mitigating Motion Blur in Neural Radiance Fields with Events and Frames](https://arxiv.org/abs/2403.19780) | [EvDeblurNeRF](https://github.com/uzh-rpg/EvDeblurNeRF) |

| CVPR | [Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring](https://arxiv.org/abs/2404.13153) | [MISCFilter](https://github.com/ChengxuLiu/MISCFilter) |

| CVPR  | [Motion Blur Decomposition with Cross-shutter Guidance](https://arxiv.org/abs/2404.01120) | [dualBR](https://github.com/jixiang2016/dualBR) |

| CVPR  | [Spike-guided Motion Deblurring with Unknown Modal Spatiotemporal Alignment](https://openaccess.thecvf.com//content/CVPR2024/html/Zhang_Spike-guided_Motion_Deblurring_with_Unknown_Modal_Spatiotemporal_Alignment_CVPR_2024_paper.html) | [UaSDN](https://github.com/Leozhangjiyuan/UaSDN) |

| CVPR  | [Blur-aware Spatio-temporal Sparse Transformer for Video Deblurring](https://arxiv.org/abs/2406.07551) | [BSSTNet](https://github.com/huicongzhang/BSSTNet) |

| CVPR | [Unsupervised Blind Image Deblurring Based on Self-Enhancement](https://openaccess.thecvf.com//content/CVPR2024/html/Chen_Unsupervised_Blind_Image_Deblurring_Based_on_Self-Enhancement_CVPR_2024_paper.html) | - |

| CVPR  | [Real-World Efficient Blind Motion Deblurring via Blur Pixel Discretization](https://arxiv.org/abs/2404.12168) | - |

| CVPR  | [EVS-assisted Joint Deblurring Rolling-Shutter Correction and Video Frame Interpolation through Sensor Inverse Modeling](https://openaccess.thecvf.com/content/CVPR2024/papers/Jiang_EVS-assisted_Joint_Deblurring_Rolling-Shutter_Correction_and_Video_Frame_Interpolation_through_CVPR_2024_paper.pdf) | - |

| CVPR  | [Latency Correction for Event-guided Deblurring and Frame Interpolation](https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Latency_Correction_for_Event-guided_Deblurring_and_Frame_Interpolation_CVPR_2024_paper.pdf) | - |

| CVPR  | [Frequency-aware Event-based Video Deblurring for Real-World Motion Blur](https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Frequency-aware_Event-based_Video_Deblurring_for_Real-World_Motion_Blur_CVPR_2024_paper.html) | - |

| arXiv     | [Gyroscope-Assisted Motion Deblurring Network](https://arxiv.org/abs/2402.06854) | - |

| arXiv     | [Gyro-based Neural Single Image Deblurring](https://arxiv.org/abs/2404.00916) | - |

| ECCV  | [BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting](https://arxiv.org/abs/2403.11831) | [BAD-Gaussians](https://github.com/WU-CVGL/BAD-Gaussians) |

| ECCV  | [BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event Stream](https://arxiv.org/abs/2407.02174v2) | [BeNeRF](https://github.com/WU-CVGL/BeNeRF) |

| ECCV  | [Blind image deblurring with noise-robust kernel estimation](https://github.com/csleemooo/BD_noise_robust_kernel_estimation) | [BD_noise_robust_kernel_estimation](https://github.com/csleemooo/BD_noise_robust_kernel_estimation) |

| ECCV  | [Domain-adaptive Video Deblurring via Test-time Blurring](https://arxiv.org/abs/2407.09059) | [DADeblur](https://github.com/Jin-Ting-He/DADeblur) |

| ECCV  | [Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for Natural Camera Motion](https://arxiv.org/abs/2403.13327) | [3dgs-deblur](https://github.com/SpectacularAI/3dgs-deblur) |

| ECCV  | [Towards Real-world Event-guided Low-light Video Enhancement and Deblurring](http://vi.kaist.ac.kr/2024/07/02/towards-real-world-event-guided-low-light-video-enhancement-and-deblurring/) | [ELEDNet](https://github.com/intelpro/ELEDNet) |

| ECCV  | [UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and Interpolation](https://arxiv.org/abs/2305.15078) | [UniINR](https://github.com/yunfanLu/UniINR) |

## 2023 Papers

| Venue | Paper | Link |

|-------|-------|------|

| ICML | [GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration](https://arxiv.org/abs/2301.12686v2) | [GibbsDDRM](https://github.com/sony/gibbsddrm) |

| IJCV | [Blind Image Deblurring with Unknown Kernel Size and Substantial Noise](https://arxiv.org/abs/2208.09483v2) | [Blind Image Deblurring](https://github.com/sun-umn/Blind-Image-Deblurring) |

| TIP | [INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions](https://ieeexplore.ieee.org/document/10047966) | [INFWIDE](https://github.com/zhihongz/infwide) |

| AAAI      | [Real-World Deep Local Motion Deblurring](https://arxiv.org/abs/2204.08179) | [ReLoBlur](https://github.com/LeiaLi/ReLoBlur) |

| ICCV      | [Multi-scale Residual Low-Pass Filter Network for Image Deblurring](https://ieeexplore.ieee.org/document/10377577) | - |

| TCSVT     | [Multi-Scale Frequency Separation Network for Image Deblurring](https://arxiv.org/abs/2206.00798) | [MSFS-Net](https://github.com/LiQiang0307/MSFS-Net) |

| ICML      | [IRNeXt: Rethinking Convolutional Network Design for Image Restoration](https://dl.acm.org/doi/10.5555/3618408.3618669) | [IRNeXt](https://github.com/c-yn/IRNeXt) |

| CVPR  | [Structured Kernel Estimation for Photon-Limited Deconvolution](https://arxiv.org/abs/2303.03472) | [structured-kernel-cvpr23](https://github.com/sanghviyashiitb/structured-kernel-cvpr23) |

| CVPR  | [Blur Interpolation Transformer for Real-World Motion from Blur](https://arxiv.org/abs/2211.11423) | [BiT](https://github.com/zzh-tech/BiT) |

| CVPR  | [Neumann Network with Recursive Kernels for Single Image Defocus Deblurring](https://openaccess.thecvf.com/content/CVPR2023/papers/Quan_Neumann_Network_With_Recursive_Kernels_for_Single_Image_Defocus_Deblurring_CVPR_2023_paper.pdf) | [NRKNet](https://github.com/csZcWu/NRKNet) |

| CVPR  | [Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring](https://arxiv.org/abs/2211.12250) | [FFTformer](https://github.com/kkkls/FFTformer) |

| CVPR  | [Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur](https://arxiv.org/abs/2304.12652) | [HybridNeuralRendering](https://github.com/CVMI-Lab/HybridNeuralRendering) |

| CVPR  | [Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring](https://openaccess.thecvf.com/content/CVPR2023/html/Fang_Self-Supervised_Non-Uniform_Kernel_Estimation_With_Flow-Based_Motion_Prior_for_Blind_CVPR_2023_paper.html) | [UFPDeblur](https://github.com/Fangzhenxuan/UFPDeblur) |

| CVPR  | [Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior](https://arxiv.org/abs/2210.05361) | [UAUDeblur](https://github.com/xl-tang01/UAUDeblur) |

| CVPR  | [K3DN: Disparity-Aware Kernel Estimation for Dual-Pixel Defocus Deblurring](https://openaccess.thecvf.com/content/CVPR2023/html/Yang_K3DN_Disparity-Aware_Kernel_Estimation_for_Dual-Pixel_Defocus_Deblurring_CVPR_2023_paper.html) | - |

| CVPR  | [Self-Supervised Blind Motion Deblurring With Deep Expectation Maximization](https://ieeexplore.ieee.org/document/10203880) | - |

| CVPR  | [HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering](https://arxiv.org/abs/2304.01686) | [HyperCUT](https://github.com/VinAIResearch/HyperCUT) |

| CVPR  | [Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring](https://ieeexplore.ieee.org/document/10204041) | [DSTNet](https://github.com/xuboming8/DSTNet) |

| AAAI  | [Dual-Domain Attention for Image Deblurring](https://ojs.aaai.org/index.php/AAAI/article/view/25122) | [DDANet](https://github.com/c-yn/DDANet) |

| AAAI  | [Real-World Deep Local Motion Deblurring](https://arxiv.org/abs/2204.08179) | [ReLoBlur](https://github.com/LeiaLi/ReLoBlur) |

| AAAI  | [Learning Single Image Defocus Deblurring with Misaligned Training Pairs](https://ojs.aaai.org/index.php/AAAI/article/view/25235) | [JDRL](https://github.com/liyucs/JDRL) |

| AAAI  | [Intriguing Findings of Frequency Selection for Image Deblurring](https://arxiv.org/abs/2111.11745) | [DeepRFT](https://github.com/INVOKERer/DeepRFT/tree/AAAI2023) |

| AAAI  | [Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild](https://ojs.aaai.org/index.php/AAAI/article/view/25446) | - |

| ICCV  | [Multiscale Structure Guided Diffusion for Image Deblurring](https://arxiv.org/abs/2212.01789) | - |

| ICCV  | [Single Image Defocus Deblurring via Implicit Neural Inverse Kernels](https://openaccess.thecvf.com/content/ICCV2023/papers/Quan_Single_Image_Defocus_Deblurring_via_Implicit_Neural_Inverse_Kernels_ICCV_2023_paper.pdf) | [INIKNet](https://github.com/xinyao240/INIKNet) |

| ICCV  | [Single Image Deblurring with Row-dependent Blur Magnitude](https://openaccess.thecvf.com//content/ICCV2023/html/Ji_Single_Image_Deblurring_with_Row-dependent_Blur_Magnitude_ICCV_2023_paper.html) | [RSS-T](https://github.com/jixiang2016/RSS-T) |

| ICCV  | [Non-Coaxial Event-Guided Motion Deblurring with Spatial Alignment](https://openaccess.thecvf.com/content/ICCV2023/html/Cho_Non-Coaxial_Event-Guided_Motion_Deblurring_with_Spatial_Alignment_ICCV_2023_paper.html) | - |

| ICCV  | [Generalizing Event-Based Motion Deblurring in Real-World Scenarios](https://arxiv.org/abs/2308.05932) | [GEM](https://github.com/XiangZ-0/GEM) |

| ICCV  | [Exploring Temporal Frequency Spectrum in Deep Video Deblurring](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhu_Exploring_Temporal_Frequency_Spectrum_in_Deep_Video_Deblurring_ICCV_2023_paper.pdf) | - |

| NeurIPS | [Hierarchical Integration Diffusion Model for Realistic Image Deblurring](https://arxiv.org/abs/2305.12966) | [HI-Diff](https://github.com/zhengchen1999/HI-Diff) |

| NeurIPS| [Enhancing Motion Deblurring in High-Speed Scenes with Spike Streams](https://openreview.net/forum?id=cAyLnMxiTl) | - |

## 2022 Papers

| Venue | Paper | Link |

|-------|-------|------|

| ECCVW | [MSSNet: Multi-Scale-Stage Network for Single Image Deblurring](https://arxiv.org/abs/2202.09652) | [MSSNet](https://github.com/kky7/MSSNet) |

| CVPRW | [HINet: Half Instance Normalization Network for Image Restoration](https://arxiv.org/abs/2105.06086) | [HINet](https://github.com/megvii-model/HINet) |

| TIP   | [BANet: A Blur-Aware Attention Network for Dynamic Scene Deblurring](https://arxiv.org/abs/2101.07518) | [BANet](https://github.com/pp00704831/BANet-TIP-2022) |

| CVPR  | [Learning to Deblur using Light Field Generated and Real Defocus Images](https://arxiv.org/abs/2204.00367) | [DRBNet](https://github.com/lingyanruan/DRBNet) |

| CVPR  | [Pixel Screening Based Intermediate Correction for Blind Deblurring](https://ieeexplore.ieee.org/document/9878959) | - |

| CVPR  | [Deblurring via Stochastic Refinement](https://openaccess.thecvf.com/content/CVPR2022/html/Whang_Deblurring_via_Stochastic_Refinement_CVPR_2022_paper.html) | - |

| CVPR  | [XYDeblur: Divide and Conquer for Single Image Deblurring](https://ieeexplore.ieee.org/document/9880408) | - |

| CVPR  | [Unifying Motion Deblurring and Frame Interpolation with Events](https://arxiv.org/abs/2203.12178) | [EVDI](https://github.com/XiangZ-0/EVDI) |

| CVPR  | [E-CIR: Event-Enhanced Continuous Intensity Recovery](https://arxiv.org/abs/2203.01935) | [E-CIR](https://github.com/chensong1995/E-CIR) |

| CVPR  | [Multi-Scale Memory-Based Video Deblurring](https://arxiv.org/abs/2203.01935) | [MemDeblur](https://github.com/jibo27/MemDeblur) |

| ECCV  | [Learning Degradation Representations for Image Deblurring](https://arxiv.org/abs/2208.05244) | [Learning_degradation](https://github.com/dasongli1/Learning_degradation) |

| ECCV  | [Stripformer: Strip Transformer for Fast Image Deblurring](https://arxiv.org/abs/2204.04627) | [Stripformer-ECCV-2022](https://github.com/pp00704831/Stripformer-ECCV-2022-) |

| ECCV  | [Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance](https://arxiv.org/abs/2207.10123) | [Animation-from-Blur](https://github.com/zzh-tech/Animation-from-Blur) |

| ECCV  | [United Defocus Blur Detection and Deblurring via Adversarial Promoting Learning](https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/3308_ECCV_2022_paper.php) | [APL](https://github.com/wdzhao123/APL) |

| ECCV  | [Realistic Blur Synthesis for Learning Image Deblurring](https://arxiv.org/abs/2202.08771) | [RSBlur](https://github.com/rimchang/RSBlur) |

| ECCV  | [Event-based Fusion for Motion Deblurring with Cross-modal Attention](https://arxiv.org/abs/2112.00167) | [EFNet](https://github.com/AHupuJR/EFNet) |

| ECCV  | [Event-Guided Deblurring of Unknown Exposure Time Videos](https://arxiv.org/abs/2112.06988) | [UEVD_public](https://github.com/intelpro/UEVD_public) |

| ECCV  | [Spatio-Temporal Deformable Attention Network for Video Deblurring](https://arxiv.org/abs/2207.10852) | [STDAN](https://github.com/huicongzhang/STDAN) |

| ECCV  | [Efficient Video Deblurring Guided by Motion Magnitude](https://arxiv.org/abs/2207.13374) | [MMP-RNN](https://github.com/sollynoay/MMP-RNN) |

| ECCV  | [ERDN: Equivalent Receptive Field Deformable Network for Video Deblurring](https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/4085_ECCV_2022_paper.php) | [ERDN](https://github.com/TencentCloud/ERDN) |

| ECCV  | [DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting](https://arxiv.org/abs/2111.09985) | [DeMFI](https://github.com/JihyongOh/DeMFI) |

| ECCV  | [Towards Real-World Video Deblurring by Exploring Blur Formation Process](https://arxiv.org/abs/2208.13184) | [RAWBlur](https://github.com/ljzycmd/rawblur) |

## 2021 Papers

| Venue | Paper | Link |

|-------|-------|------|

| CVPR | [Explore Image Deblurring via Encoded Blur Kernel Space](https://openaccess.thecvf.com//content/CVPR2021/html/Tran_Explore_Image_Deblurring_via_Encoded_Blur_Kernel_Space_CVPR_2021_paper.html) | [Blur-Kernel-Space-Exploring](https://github.com/VinAIResearch/blur-kernel-space-exploring) |

| ICCV  | [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://arxiv.org/abs/2108.05054) | [MIMO-UNet](https://github.com/chosj95/MIMO-UNet) |

| CVPR  | [Multi-Stage Progressive Image Restoration](https://arxiv.org/abs/2102.02808) | [MPRNet](https://github.com/swz30/MPRNet) |

| CVPR  | [DeFMO: Deblurring and Shape Recovery of Fast Moving Objects](https://arxiv.org/abs/2012.00595) | [DeFMO](https://github.com/rozumden/DeFMO) |

| CVPR  | [ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring](https://arxiv.org/abs/2103.04260) | - |

| CVPR  | [Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes](https://arxiv.org/abs/2104.01601) | [RSCD](https://github.com/zzh-tech/RSCD) |

| CVPR  | [Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times](https://arxiv.org/abs/2012.04515) | [Digital Gimbal](https://github.com/omer11a/digital-gimbal) |

| ICCV  | [Bringing Events into Video Deblurring with Non consecutively Blurry Frames](https://ieeexplore.ieee.org/document/9711143) | [D2Net](https://github.com/shangwei5/D2Net) |

| ICCV  | [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://arxiv.org/abs/2108.05054) | [MIMO-UNet](https://github.com/chosj95/MIMO-UNet) |

| ICCV  | [Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions](https://arxiv.org/abs/2108.09108) | [KPAC](https://github.com/HyeongseokSon1/KPAC) |

| NeurIPS | [Gaussian Kernel Mixture Network for Single Image Defocus Deblurring](https://openreview.net/forum?id=kSR-_SVzDR-) | [GKMNet](https://github.com/csZcWu/GKMNet) |

## 2020 Papers

| Venue | Paper | Link |

|-------|-------|------|

| NeurIPS | [Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring](https://arxiv.org/abs/2103.09962v1) | [DWDN](https://gitlab.mpi-klsb.mpg.de/jdong/dwdn) |

| IEEE | [Raw Image Deblurring](https://arxiv.org/abs/2012.04264v1) | [Raw Image Deblurring](https://github.com/bob831009/raw_image_deblurring) |

| TCSVT | [A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image Deblurring](https://arxiv.org/abs/1906.06642v5) | [Deblur-PMP](https://github.com/FWen/deblur-pmp) |

| ECCV | [End-to-end Interpretable Learning of Non-blind Image Deblurring](https://arxiv.org/abs/2007.01769v2) | [CPCR](https://github.com/teboli/CPCR) |

| IJCV | [Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks](https://arxiv.org/abs/1902.07474v2) | [DAU-ConvNet](https://github.com/skokec/DAU-ConvNet) |

| TNNLS | [Learning Deep Gradient Descent Optimization for Image Deconvolution](https://arxiv.org/abs/1804.03368v2) | [Learn-Optimizer-RGDN](https://github.com/donggong1/learn-optimizer-rgdn) |

| TCSVT | [Deep Convolutional-Neural-Network-Based Channel Attention for Single Image Dynamic Scene Blind Deblurring](https://ieeexplore.ieee.org/document/9247132) | - |

| CVPR  | [Cascaded Deep Video Deblurring Using Temporal Sharpness Prior](https://arxiv.org/abs/2004.02501) | [CDVD-TSP](https://github.com/csbhr/CDVD-TSP) |

| CVPR  | [Learning Event-Based Motion Deblurring](https://ieeexplore.ieee.org/document/9156741) | - |

| CVPR  | [Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring](https://ieeexplore.ieee.org/document/9157497) | [VEM-NBD](https://github.com/ysnan/VEM-NBD) |

| CVPR  | [Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yuan_Efficient_Dynamic_Scene_Deblurring_Using_Spatially_Variant_Deconvolution_Network_With_CVPR_2020_paper.pdf) | - |

| CVPR  | [Deblurring by Realistic Blurring](https://arxiv.org/abs/2004.01860) | [Deblurring-by-Realistic-Blurring](https://github.com/HDCVLab/Deblurring-by-Realistic-Blurring) |

| CVPR  | [Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring](https://arxiv.org/abs/2004.05343) | - |

| CVPR  | [Deblurring Using Analysis-Synthesis Networks Pair](https://arxiv.org/abs/2004.02956) | - |

| ECCV  | [Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510188.pdf) | [ESTRNN](https://github.com/zzh-tech/ESTRNN) |

| ECCV  | [Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510324.pdf) | [MTRNN](https://github.com/Dong1P/MTRNN) |

| ECCV  | [Learning Event-Driven Video Deblurring and Interpolation](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530681.pdf) | [LEDVDI](https://github.com/Lynn0306/LEDVDI) |

| ECCV  | [Defocus Deblurring Using Dual-Pixel Data](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550120.pdf) | [defocus-deblurring-dual-pixel](https://github.com/Abdullah-Abuolaim/defocus-deblurring-dual-pixel) |

| ECCV  | [Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700188.pdf) | [RealBlur](https://github.com/rimchang/RealBlur) |

| ECCV  | [OID: Outlier Identifying and Discarding in Blind Image Deblurring](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700596.pdf) | [OID](https://drive.google.com/file/d/19PCEXVs6imWqqae37r5By-OCUlrNGHnd/view) |

| ECCV  | [Enhanced Sparse Model for Blind Deblurring](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700630.pdf) | [Enhanced Sparse Model](https://drive.google.com/file/d/1HgLrWWh0Lx69kRh8xkm_peqNha1lNIOG/view) |

## 2019 Papers

| Venue | Paper | Link |

|-------|-------|------|

| ICCV | [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://arxiv.org/abs/1908.03826v1) | [DeblurGANv2](https://github.com/VITA-Group/DeblurGANv2) |

| BMVC | [Blind Image Deconvolution using Pretrained Generative Priors](https://arxiv.org/abs/1908.07404v1) | [Blind Image Deconvolution](https://github.com/axium/Blind-Image-Deconvolution-using-Deep-Generative-Priors) |

| arxiv | [Efficient Blind Deblurring under High Noise Levels](https://arxiv.org/abs/1904.09154v2) | [High-Noise-Deblurring](https://github.com/kidanger/high-noise-deblurring) |

| CVPR      | [Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring](https://arxiv.org/abs/1904.03468) | [DMPHN](https://github.com/HongguangZhang/DMPHN-cvpr19-master) |

| CVPR      | [Dynamic Scene Deblurring with Parameter Selective Sharing and Nested Skip Connections](https://ieeexplore.ieee.org/document/8953950) | [Deblur](https://github.com/firenxygao/deblur) |

## Datasets

| Name      | Description                                                                                                                                                                                                                      | Link                                                                                             |

|-----------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|

| GoPro     | The GoPro dataset consists of 3,214 pairs of motion-blurred and sharp images, each with a resolution of 1,280×720 pixels, divided into 2,103 training pairs and 1,111 test pairs.                                           | [GoPro](https://seungjunnah.github.io/Datasets/gopro)                                            |

| REDS      | The REalistic and Dynamic Scenes (REDS) dataset is generated from 120 fps videos, with blurry frames synthesized by merging consecutive frames, capturing realistic motion blur in dynamic scenes.                      | [REDS](https://seungjunnah.github.io/Datasets/reds)                                             |

| DPDD      | The Dual-Pixel Defocus Deblurring (DPDD) dataset contains 500 carefully captured scenes, comprising 2000 images in total: 500 defocus-blurred images with their 1000 dual-pixel (DP) sub-aperture views and 500 corresponding all-in-focus images, all at full-frame resolution of 6720x4480 pixels. | [DPDD](https://abuolaim.nowaty.com/eccv_2020_dp_defocus_deblurring/)                           |

| HIDE      | The HIDE (Human-aware Image Deblurring) dataset consists of 8,422 blurred images paired with their corresponding sharp images, focusing on motion deblurring with an emphasis on human subjects, making it ideal for human-centric deblurring tasks. | [HIDE](https://github.com/joanshen0508/HA_deblur)                                                                                           |

| RealBlur  | The RealBlur dataset consists of 4,738 pairs of images from 232 different scenes, captured in both camera raw and JPEG formats. It is divided into two subsets: RealBlur-R with raw images and RealBlur-J with JPEG images, with 3,758 training pairs and 980 test pairs in each subset. | [RealBlur](https://cg.postech.ac.kr/research/realblur/)                                        |

| CelebA    | The CelebFaces Attributes dataset (CelebA) is a large-scale face attributes dataset comprising 202,599 images of 10,177 celebrities. Each image is 178×218 pixels and annotated with 40 binary labels for facial attributes like hair color, gender, and age. | [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)                                   |

| Deblur-NeRF | The Deblur-NeRF dataset focuses on two types of blur: camera motion blur and defocus blur. It includes 5 synthesized scenes for each blur type, created using Blender with multi-view cameras to simulate real data capture. For motion blur, images are rendered from interpolated camera poses, while defocus blur images are generated with depth-of-field effects. Additionally, the dataset features 20 real-world scenes—10 for each blur type—captured with a Canon EOS RP, including both manually blurred images and sharp reference images. | [Deblur-NeRF](https://limacv.github.io/deblurnerf/)                                           |

| RSBlur    | The RSBlur dataset offers pairs of real and synthetic blurred images, each with corresponding ground truth sharp images. It is designed to evaluate deblurring and blur synthesis methods on real-world blurred images, with training, validation, and test sets comprising 8,878, 1,120, and 3,360 blurred images, respectively. | [RSBlur](https://cg.postech.ac.kr/research/rsblur/)                                           |

| ReloBlur | The ReloBlur dataset for local motion deblurring consists of 2405 blurred images with the size of 2152×1436 that are divided into 2010 training images and 395 test images. The dataset consists of pairs of a realistic locally blurred image and the corresponding ground truth sharp image that are obtained by a synchronized beam-splitting photographing system. For efficient training and testing, we also provide the resized version of ReLoBlur Dataset with the size of 538x359. | [ReloBrur](https://leiali.github.io/ReLoBlur_homepage/index.html) |