JPEG Artifact Removal

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Most implemented papers archive 2025-07-28

12 shown of 12 papers with code (20 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.

• Projected Distribution Loss for Image Enhancement
  16 Dec 2020 4 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  More explicitly, we show that in imaging applications such as denoising, super-resolution, demosaicing, deblurring and JPEG artifact removal, the proposed learning loss outperforms the current state-of-the-art on…
• 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.
• Compression-Aware One-Step Diffusion Model for JPEG Artifact Removal
  14 Feb 2025 1 repository listed Syntology ran 7 of 11 samples · 4 unverified
  The core of CODiff is the compression-aware visual embedder (CaVE), which extracts and leverages JPEG compression priors to guide the diffusion model.
• JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients
  3 Apr 2024 1 repository listed
  We propose a practical approach to JPEG image decoding, utilizing a local implicit neural representation with continuous cosine formulation.
• Bidirectional Consistency Models
  26 Mar 2024 1 repository listed Syntology ran 2 of 6 samples · 4 unverified · 1 pointer-only (licence)
  Interestingly, DMs can also invert an input image to noise by moving backward along the PF ODE, a key operation for downstream tasks such as interpolation and image editing.
• Controlling Vision-Language Models for Multi-Task Image Restoration
  2 Oct 2023 1 repository listed Syntology ran 8 of 9 samples · 1 unverified
  In this paper, we present a degradation-aware vision-language model (DA-CLIP) to better transfer pretrained vision-language models to low-level vision tasks as a multi-task framework for image restoration.
• Restore Anything Pipeline: Segment Anything Meets Image Restoration
  22 May 2023 1 repository listed
  In this paper, we introduce the Restore Anything Pipeline (RAP), a novel interactive and per-object level image restoration approach that incorporates a controllable model to generate different results that users may…
• Removing Image Artifacts From Scratched Lens Protectors
  11 Feb 2023 1 repository listed
  Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts.
• DriftRec: Adapting diffusion models to blind JPEG restoration
  12 Nov 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  In this work, we utilize the high-fidelity generation abilities of diffusion models to solve blind JPEG restoration at high compression levels.
• 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.
• Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable
  7 Sep 2020 1 repository listed
  Identifying the information lossless condition for deep neural architectures is important, because tasks such as image restoration require keep the detailed information of the input data as much as possible.
• Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network
  10 Jul 2019 1 repository listed
  The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors.

Syntology lines on 7 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.