5-Degradation Blind All-in-One Image Restoration

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 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.

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

No dataset record in the archive lists this task.

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

7 shown of 7 papers with code (8 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.

• Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restoration
  3 Nov 2024 2 repositories listed Syntology ran 6 of 6 samples · 0 unverified · 6 pointer-only (licence)
  More crucially, we design the transport map for restoration as a two-pass DA-RCOT map, in which the transport residual is computed in the first pass and then encoded as multi-scale residual embeddings to condition the…
• Universal Image Restoration Pre-training via Degradation Classification
  26 Jan 2025 1 repository listed Syntology ran 5 of 8 samples · 3 unverified · 8 pointer-only (licence)
  Subsequently, a lightweight decoder, such as ResNet18, is leveraged to classify the degradation type of the input image solely based on the features extracted in the first stage, without utilizing the input image.
• Adaptive Blind All-in-One Image Restoration
  27 Nov 2024 1 repository listed
  Blind all-in-one image restoration models aim to recover a high-quality image from an input degraded with unknown distortions.
• HAIR: Hypernetworks-based All-in-One Image Restoration
  15 Aug 2024 1 repository listed Syntology ran 13 of 18 samples · 5 unverified · 18 pointer-only (licence)
  To alleviate this issue, we propose HAIR, a Hypernetworks-based All-in-One Image Restoration plug-and-play method that generates parameters based on the input image and thus makes the model to adapt to specific…
• Restore Anything Model via Efficient Degradation Adaptation
  18 Jul 2024 1 repository listed
  With the proliferation of mobile devices, the need for an efficient model to restore any degraded image has become increasingly significant and impactful.
• Ingredient-Oriented Multi-Degradation Learning for Image Restoration
  1 Jan 2023 1 repository listed
  Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation.
• All-in-One Image Restoration for Unknown Corruption
  1 Jan 2022 1 repository listed
  In this paper, we study a challenging problem in image restoration, namely, how to develop an all-in-one method that could recover images from a variety of unknown corruption types and levels.

Syntology lines on 3 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.