Explore Image Deblurring via Blur Kernel Space
Not on the map. Why?
Cited by 26 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen blur operator by a kernel in the encoded space and searches for the corresponding sharp image. Unlike recent deep-learning-based methods, our system can handle unseen blur kernel, while avoiding using complicated handcrafted priors on the blur operator often found in classical methods. Due to the method's design, the encoded kernel space is fully differentiable, thus can be easily adopted in deep neural network models. Moreover, our method can be used for blur synthesis by transferring existing blur operators from a given dataset into a new domain. Finally, we provide experimental results to confirm the effectiveness of the proposed method.
Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). Builds on 27 of its 51 references that are in the map. First posted on arXiv 1 Apr 2021.
Builds on
27 of 51 references.
References · 27
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
- 2016Feature Pyramid Networks for Object Detectioncited by 5,021
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
- 2014Deep Learning Face Attributes in the Wildcited by 3,679
- 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
- 2017Deep Image Priorcited by 1,097
- 2016Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurringcited by 722
- 2019EDVR: Video Restoration with Enhanced Deformable Convolutional Networkscited by 492
15 more
- 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 452
- 2017DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networkscited by 366
- 2019DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Bettercited by 361
- 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
- 2018Scale-recurrent Network for Deep Image Deblurringcited by 332
- 2020Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulationcited by 225
- 2018"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priorscited by 114
- 2019Neural Blind Deconvolution Using Deep Priorscited by 113
- 2019Spatio-Temporal Filter Adaptive Network for Video Deblurringcited by 106
- 2018Deep Semantic Face Deblurringcited by 80
- 2016A Neural Approach to Blind Motion Deblurringcited by 69
- 2014Learning to Deblurcited by 66
- 2018Image Restoration using Total Variation Regularized Deep Image Priorcited by 65
- 2019Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networkscited by 24
- 2018Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancementcited by 14
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Citing papers per year: 2021: 4; 2022: 10; 2023: 8; 2024: 16; 2025: 10; 2026: 10.
58 papers.
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- 2025Variational Control for Guidance in Diffusion Modelscited by 22
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- 2021Dense Uncertainty Estimationcited by 9
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- 2023Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networkscited by 7
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- 2024Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpointcited by 5
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- 2024Posterior sampling via Langevin dynamics based on generative priorscited by 5
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- 2026Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance30 Jan 2026
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- 2025Improving Diffusion-based Inverse Algorithms under Few-Step Constraint via Learnable Linear Extrapolation13 Mar 2025
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- 2024Enhancing and Accelerating Diffusion-Based Inverse Problem Solving through Measurements Optimization5 Dec 2024
- 2024Beyond Pixels: Text Enhances Generalization in Real-World Image Restoration1 Dec 2024
- 2024Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint1 Dec 2024
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- 2024Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing1 Jul 2024
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- 2026Image Restoration via Diffusion Models with Dynamic Resolution14 May 2026
- 2026Outlier-Robust Diffusion Solvers for Inverse Problems10 May 2026
- 2026Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning18 Apr 2026
- 2026Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework10 Mar 2026
- 2026Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance30 Jan 2026
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- 2025PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems25 Sep 2025
- 2025EquiReg: Equivariance Regularized Diffusion for Inverse Problems29 May 2025
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- 2022Event-based Image Deblurring with Dynamic Motion Awarenesscited by 10
- 2021Dense Uncertainty Estimationcited by 9
- 2024Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domainscited by 9
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- 2026Solving Inverse Problems with Flow-based Models via Model Predictive Control30 Jan 2026
- 2025Blur2seq: Blind Deblurring and Camera Trajectory Estimation from a Single Camera Motion-blurred Image23 Oct 2025
- 2025RED: Robust Event-Guided Motion Deblurring with Modality-Specific Disentanglement6 Sep 2025
- 2025UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights16 May 2025
- 2025Improving Diffusion-based Inverse Algorithms under Few-Step Constraint via Learnable Linear Extrapolation13 Mar 2025
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