JPEG Artifact Correction

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

39 leaderboard tables shown for this task, 39 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 39 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

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

5 datasets 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.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

12 shown of 12 papers with code (18 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.

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
  13 Aug 2016 22 repositories listed Syntology ran 1 of 6 samples · 5 unverified
  Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
• Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
  29 Jun 2016 17 repositories listed
  In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.
• Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
  22 Sep 2022 5 repositories listed
  Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data.
• Multi-level Wavelet-CNN for Image Restoration
  18 May 2018 5 repositories listed
  With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork.
• Compression Artifacts Reduction by a Deep Convolutional Network
  27 Apr 2015 4 repositories listed Syntology ran 0 of 12 samples · 12 unverified
  Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring.
• Residual Dense Network for Image Restoration
  25 Dec 2018 3 repositories listed
  We fully exploit the hierarchical features from all the convolutional layers.
• Towards Flexible Blind JPEG Artifacts Removal
  29 Sep 2021 2 repositories listed Syntology ran 9 of 11 samples · 2 unverified
  Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention due to its convenience for practical usage.
• MemNet: A Persistent Memory Network for Image Restoration
  7 Aug 2017 2 repositories listed
  We apply MemNet to three image restoration tasks, i.
• JPEG Artifact Correction using Denoising Diffusion Restoration Models
  23 Sep 2022 1 repository listed Syntology ran 8 of 12 samples · 4 unverified
  Diffusion models can be used as learned priors for solving various inverse problems.
• Hypernetwork-Based Adaptive Image Restoration
  13 Jun 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model.
• Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal
  15 Sep 2020 1 repository listed
  Our proposed network is a single model approach that can be trained for handling a wide range of quality factors while consistently delivering superior or comparable image artifacts removal performance.
• Quantization Guided JPEG Artifact Correction
  17 Apr 2020 1 repository listed
  The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios.

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.