Blind Image Deblurring

Description from the archive archive 2025-07-28.

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

19 shown of 19 papers with code (70 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.

• Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks
  20 Feb 2019 3 repositories listed
  Convolutional neural networks excel in a number of computer vision tasks.
• Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser Network
  1 Sep 2024 1 repository listed
  In data driven deep learning, distributed sensing and joint computing bring heavy load for computing and communication.
• Blind Image Deblurring with FFT-ReLU Sparsity Prior
  12 Jun 2024 1 repository listed
  Blind image deblurring is the process of recovering a sharp image from a blurred one without prior knowledge about the blur kernel.
• A Fast Blind Deblurring Algorithm Using Local Gradient Product Prior
  18 Mar 2024 1 repository listed
  Blind image deblurring is the restoration of latent clear images from blurred images without knowing the blur kernel.
• Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution
  1 Sep 2023 1 repository listed Syntology ran 12 of 25 samples · 13 unverified · 25 pointer-only (licence)
  Our method alternates between approximating the expected log-likelihood of the inverse problem using samples drawn from a diffusion model and a maximization step to estimate unknown model parameters.
• Estimation of motion blur kernel parameters using regression convolutional neural networks
  2 Aug 2023 1 repository listed
  Many deblurring and blur kernel estimation methods use a maximum a posteriori (MAP) approach or deep learning-based classification techniques to sharpen an image and/or predict the blur kernel.
• GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration
  30 Jan 2023 1 repository listed Syntology ran 12 of 20 samples · 8 unverified
  Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements.
• Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring
  1 Jan 2023 1 repository listed
  To address these issues, we propose to represent the field of motion blur kernels in a latent space by normalizing flows, and design CNNs to predict the latent codes instead of motion kernels.
• Blind Image Deblurring with Unknown Kernel Size and Substantial Noise
  18 Aug 2022 1 repository listed
  Blind image deblurring (BID) has been extensively studied in computer vision and adjacent fields.
• INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions
  17 Jul 2022 1 repository listed
  In terms of algorithm design, INFWIDE proposes a two-branch architecture, which explicitly removes noise and hallucinates saturated regions in the image space and suppresses ringing artifacts in the feature space, and…
• Explore Image Deblurring via Encoded Blur Kernel Space
  19 Jun 2021 1 repository listed
  This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space.
• Explore Image Deblurring via Blur Kernel Space
  1 Apr 2021 1 repository listed Syntology ran 3 of 7 samples · 4 unverified
  This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space.
• DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring
  18 Mar 2021 1 repository listed
  We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning.
• Raw Image Deblurring
  8 Dec 2020 1 repository listed
  Therefore, we built a new dataset containing both RAW images and processed sRGB images and design a new model to utilize the unique characteristics of RAW images.
• A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image Deblurring
  29 Oct 2020 1 repository listed
  Then, a novel algorithm is designed to efficiently exploit the sparsity of PMP in deblurring.
• End-to-end Interpretable Learning of Non-blind Image Deblurring
  3 Jul 2020 1 repository listed Syntology ran 0 of 8 samples · 8 unverified
  Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic…
• Blind Image Deconvolution using Pretrained Generative Priors
  20 Aug 2019 1 repository listed
  This paper proposes a novel approach to regularize the ill-posed blind image deconvolution (blind image deblurring) problem using deep generative networks.
• Efficient Blind Deblurring under High Noise Levels
  19 Apr 2019 1 repository listed
  In this work, we first show that current state-of-the-art kernel estimation methods based on the ℓ₀ gradient prior can be adapted to handle high noise levels while keeping their efficiency.
• Learning Deep Gradient Descent Optimization for Image Deconvolution
  10 Apr 2018 1 repository listed
  Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for…

Syntology lines on 4 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.