Image Deconvolution

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

23 shown of 23 papers with code (78 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.

• Modular proximal optimization for multidimensional total-variation regularization
  3 Nov 2014 3 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  We study \emph{TV regularization}, a widely used technique for eliciting structured sparsity.
• Deep Blind Video Super-resolution
  10 Mar 2020 2 repositories listed Syntology ran 9 of 15 samples · 6 unverified
  Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration.
• Blind Image Deconvolution by Generative-based Kernel Prior and Initializer via Latent Encoding
  20 Jul 2024 1 repository listed Syntology ran 10 of 12 samples · 2 unverified
  To alleviate this issue and further improve their performance, we propose a new framework for BID that better considers the prior modeling and the initialization for blur kernels, leveraging a deep generative model.
• Deep, convergent, unrolled half-quadratic splitting for image deconvolution
  20 Feb 2024 1 repository listed
  Through extensive experimental studies, we verify that our approach achieves competitive performance with state-of-the-art unrolled layer-specific learning and significantly improves over the traditional HQS algorithm.
• Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling
  18 Aug 2023 1 repository listed
  This discretisation is asymptotically unbiased for Gaussian targets and shown to converge in an accelerated manner for any target that is κ-strongly log-concave (i.
• Deep learning-based deconvolution for interferometric radio transient reconstruction
  24 Jun 2023 1 repository listed
  Finally, based on the test data, we evaluate the source profile reconstruction performance of the proposed methods and classical image deconvolution algorithm CLEAN applied frame-by-frame.
• Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms
  25 May 2023 1 repository listed
  We study the problem of approximate sampling from non-log-concave distributions, e.
• Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors
  28 Nov 2022 1 repository listed
  Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices.
• Galaxy Image Deconvolution for Weak Gravitational Lensing with Unrolled Plug-and-Play ADMM
  3 Nov 2022 1 repository listed
  Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements for weak gravitational lensing and galaxy evolution studies.
• Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural network
  18 May 2022 1 repository listed
  Nonblind image deconvolution (NID) is about restoring the latent image with sharp details from a noisy blurred one using a known blur kernel.
• Blind Image Deconvolution Using Variational Deep Image Prior
  1 Feb 2022 1 repository listed
  Conventional deconvolution methods utilize hand-crafted image priors to constrain the optimization.
• Wiener Guided DIP for Unsupervised Blind Image Deconvolution
  19 Dec 2021 1 repository listed
  In addition, the image generator reproduces low-frequency features of the deconvolved image faster than that of a blurry image.
• Learning Discriminative Shrinkage Deep Networks for Image Deconvolution
  27 Nov 2021 1 repository listed
  Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms and data terms of the latent clear…
• Plug-and-Play Quantum Adaptive Denoiser for Deconvolving Poisson Noisy Images
  1 Jul 2021 1 repository listed
  A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme is proposed in this paper, by embedding a recently introduced adaptive denoiser using the Schroedinger equation's solutions of quantum physics.
• Image Deconvolution via Noise-Tolerant Self-Supervised Inversion
  11 Jun 2020 1 repository listed
  We propose a general framework for solving inverse problems in the presence of noise that requires no signal prior, no noise estimate, and no clean training data.
• Microscopy Image Restoration with Deep Wiener-Kolmogorov filters
  25 Nov 2019 1 repository listed
  Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise.
• 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.
• Iterative Residual Image Deconvolution
  17 Apr 2018 1 repository listed
  Image deblurring, a.
• Simultaneous Fidelity and Regularization Learning for Image Restoration
  12 Apr 2018 1 repository listed
  For blind deconvolution, as estimation error of blur kernel is usually introduced, the subsequent non-blind deconvolution process does not restore the latent image well.
• 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…
• Blind Image Deconvolution using Deep Generative Priors
  12 Feb 2018 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  This paper proposes a novel approach to regularize the \textit{ill-posed} and \textit{non-linear} blind image deconvolution (blind deblurring) using deep generative networks as priors.
• Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
  11 Apr 2017 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks.
• A Framework for Fast Image Deconvolution with Incomplete Observations
  3 Feb 2016 1 repository listed
  In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast.

Syntology lines on 5 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.