Browse State-of-the-Art › Blind All-in-One Image Restoration
Blind All-in-One Image Restoration
10 papers with code · 2 benchmarks · 0 datasets archive 2025-07-28
Blind All-in-One Image Restoration aims to remove various degradations from an input image without prior knowledge of the degradation type or severity. This task is evaluated under two setups: three-degradation and five-degradation.
Three-Degradation Setup: The objective is to restore images affected by rain, haze, and noise. Training datasets include Rain200L for deraining, RESIDE for dehazing, and WED and BSD400 for denoising with noise levels σ = 15, 25, 50. Evaluation datasets are Rain100L for deraining, SOTS (outdoor) for dehazing, and BSD68 for denoising with σ = 15, 25, 50.
Five-Degradation Setup: This setup expands to include five common image restoration tasks: rain, haze, noise, blur, and low-light conditions. Training datasets comprise Rain200L for deraining, RESIDE for dehazing, WED and BSD400 for denoising (σ = 25), GoPro for deblurring, and LoLv1 for low-light enhancement. Evaluation uses Rain100L for deraining, SOTS (outdoor) for dehazing, BSD68 for denoising (σ = 25), GoPro for deblurring, and LoLv1 for low-light enhancement.
Performance Metrics: Model performance is assessed by reporting the average PSNR and SSIM across all evaluation datasets, reflecting the overall capability to handle diverse degradations. This task challenges models to effectively restore images across multiple degradation types without specific knowledge of the degradation, emphasizing versatility and robustness in image restoration techniques.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
2 leaderboard tables shown for this task, 2 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.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| 3-Degradations (9 rows) | ABAIR | Adaptive Blind All-in-One Image Restoration | code | — | Compare |
| 5-Degradations (9 rows) | ABAIR | Adaptive Blind All-in-One Image Restoration | code | — | Compare |
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
10 shown of 10 papers with code (12 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.
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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…
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27 Nov 2024 1 repository listedBlind all-in-one image restoration models aim to recover a high-quality image from an input degraded with unknown distortions.
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28 Sep 2024 1 repository listedOur pipeline consists of two stages: masked image pre-training and fine-tuning with mask attribute conductance.
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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…
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18 Jul 2024 1 repository listedWith the proliferation of mobile devices, the need for an efficient model to restore any degraded image has become increasingly significant and impactful.
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21 Mar 2024 1 repository listed Syntology ran 12 of 15 samples · 3 unverifiedOur approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task.
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24 Jan 2024 1 repository listedIn contrast to traditional image restoration methods, all-in-one image restoration techniques are gaining increased attention for their ability to restore images affected by diverse and unknown corruption types and…
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22 Jun 2023 1 repository listedWe present a prompt-based learning approach, PromptIR, for All-In-One image restoration that can effectively restore images from various types and levels of degradation.
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1 Jan 2023 1 repository listedLearning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation.
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1 Jan 2022 1 repository listedIn 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.
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