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

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Awesome-Deblurring-Resources

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

Overview

2024 Papers

Venue Paper Link
arxiv Blind Image Deblurring using FFT-ReLU with Deep Learning Pipeline Integration Code
arxiv Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution FastDiffusionEM
SPIE Estimation of motion blur kernel parameters using regression convolutional neural networks RegressionBlur
CVPR A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning MPT-CataBlur
CVPR AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring AdaRevD
CVPR Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains Blur2Blur
CVPR Fourier Priors-Guided Diffusion for Zero-Shot Joint Low-Light Enhancement and Deblurring FourierDiff
CVPR ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation ID-Blau
CVPR LDP: Language-driven Dual-Pixel Image Defocus Deblurring Network LDP
CVPR Mitigating Motion Blur in Neural Radiance Fields with Events and Frames EvDeblurNeRF
CVPR Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring MISCFilter
CVPR Motion Blur Decomposition with Cross-shutter Guidance dualBR
CVPR Spike-guided Motion Deblurring with Unknown Modal Spatiotemporal Alignment UaSDN
CVPR Blur-aware Spatio-temporal Sparse Transformer for Video Deblurring BSSTNet
CVPR Unsupervised Blind Image Deblurring Based on Self-Enhancement -
CVPR Real-World Efficient Blind Motion Deblurring via Blur Pixel Discretization -
CVPR EVS-assisted Joint Deblurring Rolling-Shutter Correction and Video Frame Interpolation through Sensor Inverse Modeling -
CVPR Latency Correction for Event-guided Deblurring and Frame Interpolation -
CVPR Frequency-aware Event-based Video Deblurring for Real-World Motion Blur -
arXiv Gyroscope-Assisted Motion Deblurring Network -
arXiv Gyro-based Neural Single Image Deblurring -
ECCV BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting BAD-Gaussians
ECCV BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event Stream BeNeRF
ECCV Blind image deblurring with noise-robust kernel estimation BD_noise_robust_kernel_estimation
ECCV Domain-adaptive Video Deblurring via Test-time Blurring DADeblur
ECCV Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for Natural Camera Motion 3dgs-deblur
ECCV Towards Real-world Event-guided Low-light Video Enhancement and Deblurring ELEDNet
ECCV UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and Interpolation UniINR

2023 Papers

Venue Paper Link
ICML GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration GibbsDDRM
IJCV Blind Image Deblurring with Unknown Kernel Size and Substantial Noise Blind Image Deblurring
TIP INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions INFWIDE
AAAI Real-World Deep Local Motion Deblurring ReLoBlur
ICCV Multi-scale Residual Low-Pass Filter Network for Image Deblurring -
TCSVT Multi-Scale Frequency Separation Network for Image Deblurring MSFS-Net
ICML IRNeXt: Rethinking Convolutional Network Design for Image Restoration IRNeXt
CVPR Structured Kernel Estimation for Photon-Limited Deconvolution structured-kernel-cvpr23
CVPR Blur Interpolation Transformer for Real-World Motion from Blur BiT
CVPR Neumann Network with Recursive Kernels for Single Image Defocus Deblurring NRKNet
CVPR Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring FFTformer
CVPR Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur HybridNeuralRendering
CVPR Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring UFPDeblur
CVPR Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior UAUDeblur
CVPR K3DN: Disparity-Aware Kernel Estimation for Dual-Pixel Defocus Deblurring -
CVPR Self-Supervised Blind Motion Deblurring With Deep Expectation Maximization -
CVPR HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering HyperCUT
CVPR Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring DSTNet
AAAI Dual-Domain Attention for Image Deblurring DDANet
AAAI Real-World Deep Local Motion Deblurring ReLoBlur
AAAI Learning Single Image Defocus Deblurring with Misaligned Training Pairs JDRL
AAAI Intriguing Findings of Frequency Selection for Image Deblurring DeepRFT
AAAI Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild -
ICCV Multiscale Structure Guided Diffusion for Image Deblurring -
ICCV Single Image Defocus Deblurring via Implicit Neural Inverse Kernels INIKNet
ICCV Single Image Deblurring with Row-dependent Blur Magnitude RSS-T
ICCV Non-Coaxial Event-Guided Motion Deblurring with Spatial Alignment -
ICCV Generalizing Event-Based Motion Deblurring in Real-World Scenarios GEM
ICCV Exploring Temporal Frequency Spectrum in Deep Video Deblurring -
NeurIPS Hierarchical Integration Diffusion Model for Realistic Image Deblurring HI-Diff
NeurIPS Enhancing Motion Deblurring in High-Speed Scenes with Spike Streams -

2022 Papers

Venue Paper Link
ECCVW MSSNet: Multi-Scale-Stage Network for Single Image Deblurring MSSNet
CVPRW HINet: Half Instance Normalization Network for Image Restoration HINet
TIP BANet: A Blur-Aware Attention Network for Dynamic Scene Deblurring BANet
CVPR Learning to Deblur using Light Field Generated and Real Defocus Images DRBNet
CVPR Pixel Screening Based Intermediate Correction for Blind Deblurring -
CVPR Deblurring via Stochastic Refinement -
CVPR XYDeblur: Divide and Conquer for Single Image Deblurring -
CVPR Unifying Motion Deblurring and Frame Interpolation with Events EVDI
CVPR E-CIR: Event-Enhanced Continuous Intensity Recovery E-CIR
CVPR Multi-Scale Memory-Based Video Deblurring MemDeblur
ECCV Learning Degradation Representations for Image Deblurring Learning_degradation
ECCV Stripformer: Strip Transformer for Fast Image Deblurring Stripformer-ECCV-2022
ECCV Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance Animation-from-Blur
ECCV United Defocus Blur Detection and Deblurring via Adversarial Promoting Learning APL
ECCV Realistic Blur Synthesis for Learning Image Deblurring RSBlur
ECCV Event-based Fusion for Motion Deblurring with Cross-modal Attention EFNet
ECCV Event-Guided Deblurring of Unknown Exposure Time Videos UEVD_public
ECCV Spatio-Temporal Deformable Attention Network for Video Deblurring STDAN
ECCV Efficient Video Deblurring Guided by Motion Magnitude MMP-RNN
ECCV ERDN: Equivalent Receptive Field Deformable Network for Video Deblurring ERDN
ECCV DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting DeMFI
ECCV Towards Real-World Video Deblurring by Exploring Blur Formation Process RAWBlur

2021 Papers

Venue Paper Link
CVPR Explore Image Deblurring via Encoded Blur Kernel Space Blur-Kernel-Space-Exploring
ICCV Rethinking Coarse-to-Fine Approach in Single Image Deblurring MIMO-UNet
CVPR Multi-Stage Progressive Image Restoration MPRNet
CVPR DeFMO: Deblurring and Shape Recovery of Fast Moving Objects DeFMO
CVPR ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring -
CVPR Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes RSCD
CVPR Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times Digital Gimbal
ICCV Bringing Events into Video Deblurring with Non consecutively Blurry Frames D2Net
ICCV Rethinking Coarse-to-Fine Approach in Single Image Deblurring MIMO-UNet
ICCV Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions KPAC
NeurIPS Gaussian Kernel Mixture Network for Single Image Defocus Deblurring GKMNet

2020 Papers

Venue Paper Link
NeurIPS Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring DWDN
IEEE Raw Image Deblurring Raw Image Deblurring
TCSVT A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image Deblurring Deblur-PMP
ECCV End-to-end Interpretable Learning of Non-blind Image Deblurring CPCR
IJCV Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks DAU-ConvNet
TNNLS Learning Deep Gradient Descent Optimization for Image Deconvolution Learn-Optimizer-RGDN
TCSVT Deep Convolutional-Neural-Network-Based Channel Attention for Single Image Dynamic Scene Blind Deblurring -
CVPR Cascaded Deep Video Deblurring Using Temporal Sharpness Prior CDVD-TSP
CVPR Learning Event-Based Motion Deblurring -
CVPR Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring VEM-NBD
CVPR Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training -
CVPR Deblurring by Realistic Blurring Deblurring-by-Realistic-Blurring
CVPR Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring -
CVPR Deblurring Using Analysis-Synthesis Networks Pair -
ECCV Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring ESTRNN
ECCV Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training MTRNN
ECCV Learning Event-Driven Video Deblurring and Interpolation LEDVDI
ECCV Defocus Deblurring Using Dual-Pixel Data defocus-deblurring-dual-pixel
ECCV Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms RealBlur
ECCV OID: Outlier Identifying and Discarding in Blind Image Deblurring OID
ECCV Enhanced Sparse Model for Blind Deblurring Enhanced Sparse Model

2019 Papers

Venue Paper Link
ICCV DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better DeblurGANv2
BMVC Blind Image Deconvolution using Pretrained Generative Priors Blind Image Deconvolution
arxiv Efficient Blind Deblurring under High Noise Levels High-Noise-Deblurring
CVPR Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring DMPHN
CVPR Dynamic Scene Deblurring with Parameter Selective Sharing and Nested Skip Connections 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
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
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
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
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
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
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
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
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