Image Defocus Deblurring

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

27 shown of 27 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.

• Restormer: Efficient Transformer for High-Resolution Image Restoration
  18 Nov 2021 13 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
• Intriguing Findings of Frequency Selection for Image Deblurring
  23 Nov 2021 5 repositories listed
  Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image.
• Uformer: A General U-Shaped Transformer for Image Restoration
  6 Jun 2021 4 repositories listed Syntology ran 6 of 6 samples · 0 unverified · 2 pointer-only (licence)
  Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.
• Improving Image Restoration by Revisiting Global Information Aggregation
  8 Dec 2021 3 repositories listed
  Our 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.
• Learning Single Image Defocus Deblurring with Misaligned Training Pairs
  26 Nov 2022 2 repositories listed
  First, in the deblurring module, a bi-directional optical flow-based deformation is introduced to tolerate spatial misalignment between deblurred and ground-truth images.
• EENet: Frequency-Aware and Spatially Multiscale Network for Single Image Dehazing
  1 Feb 2025 1 repository listed
  EENet comprises three primary modules: the frequency processing module, the spatial processing module, and the dual-domain interaction module.
• Reblurring-Guided Single Image Defocus Deblurring: A Learning Framework with Misaligned Training Pairs
  26 Sep 2024 1 repository listed
  Then, to effectively learn the baseline defocus deblurring network with misaligned training pairs, our reblurring module ensures spatial consistency between the deblurred image, the reblurred image and the input blurry…
• Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring
  29 Mar 2024 1 repository listed
  The CLGF module is composed of two branches: the global branch captures long-range dependency features via a selective state spaces model, while the local branch employs simplified channel attention to model local…
• Omni-Kernel Network for Image Restoration
  24 Mar 2024 1 repository listed
  Extensive experiments demonstrate that our network achieves state-of-the-art performance on 11 benchmark datasets for three representative image restoration tasks, including image dehazing, image desnowing, and image…
• Dual-domain strip attention for image restoration
  1 Mar 2024 1 repository listed
  In this paper, we develop a dual-domain strip attention mechanism for image restoration by enhancing representation learning, which consists of spatial and frequency strip attention units.
• Exploring the potential of channel interactions for image restoration
  20 Dec 2023 1 repository listed
  Image restoration aims to reconstruct a clear image from a degraded observation.
• Image Restoration via Frequency Selection
  6 Nov 2023 1 repository listed
  Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart.
• Selective Frequency Network for Image Restoration
  13 Apr 2023 1 repository listed
  Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart.
• Masked Autoencoders as Image Processors
  30 Mar 2023 1 repository listed
  Recently, masked autoencoders (MAE) for feature pre-training have further unleashed the potential of Transformers, leading to state-of-the-art performances on various high-level vision tasks.
• Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
  1 Mar 2023 1 repository listed Syntology ran 4 of 12 samples · 8 unverified
  The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration.
• Revisiting Image Deblurring with an Efficient ConvNet
  4 Feb 2023 1 repository listed
  In this work, we propose a unified lightweight CNN network that features a large effective receptive field (ERF) and demonstrates comparable or even better performance than Transformers while bearing less computational…
• Focal Network for Image Restoration
  1 Jan 2023 1 repository listed
  Image restoration aims to reconstruct a sharp image from its degraded counterpart, which plays an important role in many fields.
• Single Image Defocus Deblurring via Implicit Neural Inverse Kernels
  1 Jan 2023 1 repository listed
  Single image defocus deblurring (SIDD) is a challenging task due to the spatially-varying nature of defocus blur, characterized by per-pixel point spread functions (PSFs).
• Learning to Deblur using Light Field Generated and Real Defocus Images
  1 Apr 2022 1 repository listed Syntology ran 6 of 6 samples · 0 unverified · 6 pointer-only (licence)
  We first train the network on a light field-generated dataset for its highly accurate image correspondence.
• Gaussian Kernel Mixture Network for Single Image Defocus Deblurring
  31 Oct 2021 1 repository listed
  Defocus blur is one kind of blur effects often seen in images, which is challenging to remove due to its spatially variant amount.
• Iterative Filter Adaptive Network for Single Image Defocus Deblurring
  31 Aug 2021 1 repository listed Syntology ran 4 of 6 samples · 2 unverified · 6 pointer-only (licence)
  We propose a novel end-to-end learning-based approach for single image defocus deblurring.
• Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions
  20 Aug 2021 1 repository listed
  To utilize the property with inverse kernels, we exploit the observation that when only the size of a defocus blur changes while keeping the shape, the shape of the corresponding inverse kernel remains the same and only…
• Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning
  11 Aug 2021 1 repository listed
  Specifically, 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.
• BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
  31 May 2021 1 repository listed
  In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the DP data, which measures the defocus disparity between the left and right views.
• Attention! Stay Focus!
  16 Apr 2021 1 repository listed
  We develop a deep convolutional neural networks(CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images.
• Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data
  6 Dec 2020 1 repository listed
  Leveraging these realistic synthetic DP images, we introduce a recurrent convolutional network (RCN) architecture that improves deblurring results and is suitable for use with single-frame and multi-frame data (e.
• Defocus Deblurring Using Dual-Pixel Data
  1 May 2020 1 repository listed Syntology ran 0 of 6 samples · 6 unverified
  DP sensors are used to assist a camera's auto-focus by capturing two sub-aperture views of the scene in a single image shot.

Syntology lines on 6 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.