Browse State-of-the-Art › Single Image Deblurring
Single Image Deblurring
18 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28
Benchmarks archive 2025-07-28
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Datasets archive 2025-07-28
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Most implemented papers archive 2025-07-28
18 shown of 18 papers with code (35 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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11 Aug 2021 4 repositories listed Syntology ran 1 of 9 samples · 8 unverified · 6 pointer-only (licence)Coarse-to-fine strategies have been extensively used for the architecture design of single image deblurring networks.
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6 Feb 2018 4 repositories listedIn single image deblurring, the "coarse-to-fine" scheme, i.
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8 Dec 2021 3 repositories listedOur TLC converts global operations to local ones only during inference so that they aggregate features within local spatial regions rather than the entire large images.
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16 Apr 2025 1 repository listedThis paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results.
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9 Mar 2025 1 repository listedIn this paper, we conduct an in-depth exploration of diffusion models in deblurring and propose a one-step diffusion model for deblurring (OSDD), a novel framework that reduces the denoising process to a single step,…
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26 Dec 2024 1 repository listedIn this work (BeSplat), we demonstrate the recovery of sharp radiance field (Gaussian splats) from a single motion-blurred image and its corresponding event stream.
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26 Jan 2024 1 repository listed Syntology ran 10 of 11 samples · 1 unverifiedImage restoration tasks traditionally rely on convolutional neural networks.
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12 Jan 2023 1 repository listedWe instead propose a unified framework for event-based frame interpolation that performs deblurring ad-hoc and thus works both on sharp and blurry input videos.
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14 Jul 2022 1 repository listedWe introduce a novel framework for continuous facial motion deblurring that restores the continuous sharp moment latent in a single motion-blurred face image via a moment control factor.
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19 Feb 2022 1 repository listedBased on the analysis, we propose Multi-Scale-Stage Network (MSSNet), a novel deep learning-based approach to single image deblurring that adopts our remedies to the defects.
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1 Jan 2022 1 repository listedMany convolutional neural networks (CNNs) for single image deblurring employ a U-Net structure to estimate latent sharp images.
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11 Aug 2021 1 repository listedSpecifically, we show that jointly learning to predict the two DP views from a single blurry input image improves the network's ability to learn to deblur the image.
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10 Feb 2020 1 repository listedMotion blurry images challenge many computer vision algorithms, e.
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18 Nov 2019 1 repository listedMulti-scale (MS) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then in high spatial scale later with the…
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5 Mar 2019 1 repository listedImage deblurring aims to restore the latent sharp images from the corresponding blurred ones.
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9 Apr 2018 1 repository listedThis network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder.
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1 Jul 2017 1 repository listedWe show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.
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25 Nov 2016 1 repository listedWe show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.
Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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