A research guide to restoring images across degradations, tasks and domains.
Discover unified models, language-guided methods and restoration agents,
alongside applications in medicine, microscopy, remote sensing and beyond.
Explore the collection · Reading routes · Evaluate · What's new · Contribute
Selected work from the maintainers' research group: Unified restoration — Perceive-IR, ClearAIR, ClusIR, ProRes; Underwater restoration — UniUIR; Agentic restoration — PreActIR.
Compare methods, publication venues and available implementations in a consistent format. Papers appear once in the main tables, ordered by verified publication year or preprint year; the language-guided index connects related themes.
Choose a method family, an application domain or an evaluation resource.
| All-in-One Restoration 74 papers · Unified models, degradation representations and adaptive experts. |
Adverse Weather 31 papers · Rain, haze, snow and mixed weather degradations. |
| Agentic Restoration 11 papers · Planning, tool use, reasoning and restoration feedback. |
Medical Imaging 10 papers · Restoration and enhancement across medical modalities. |
| Remote Sensing & Hyperspectral 4 papers · Satellite imagery, spectral information and multimodal restoration. |
Microscopy & Bioimaging 3 papers · Fluorescence images, cellular detail and scientific measurements. |
| Underwater Imaging 3 papers · Color, visibility and degradation diversity underwater. |
Document Restoration 1 paper · Generalist restoration for document images. |
| Video Restoration 6 papers · Temporal information and changing weather in videos. |
Continual & Open-World 3 papers · New degradations, adaptation and remembering prior tasks. |
| Cultural Heritage 2 papers · Mural restoration under multiple degradation types. |
Downstream Vision 2 papers · Recognition and segmentation in adverse conditions. |
| Language & Instructions A thematic index to language priors, user guidance and agentic methods. |
Benchmarks & Metrics 2 reports · 19 datasets · 5 metrics. |
Four suggested reading routes through the collection:
| Your goal | Suggested route |
|---|---|
| Understand unified restoration | AirNet → PromptIR → Perceive-IR |
| Connect language to restoration | DACLIP-UIR → InstructIR → UniProcessor |
| Explore generative priors | AutoDIR → DiffUIR → RestoreVAR |
| Study planning and feedback | RAR → OPERA → Agentic restoration |
For a field-level overview, start with the related survey. For experiments, jump to datasets, challenge reports or common metrics.
Full contents · all sections
- All-in-One Image Restoration
- Adverse Weather Removal
- Vision-Language and Instruction-Guided Restoration
- Other Applications
- Downstream Vision under Adverse Weather
- Underwater Image Restoration
- Medical Image Restoration
- Microscopy and Bioimage Restoration
- Remote Sensing and Hyperspectral Restoration
- Document Image Restoration
- Cultural Heritage Image Restoration
- Agentic Image Restoration
- Video Restoration
- Continual Image Restoration
- Benchmarks and Metrics
- Related Surveys
- Contributing
Links: Code points to released implementations; Project points to informational pages.
Unified restoration methods, from degradation representations and visual prompts to experts and generative priors.
Models for rain, haze, snow, raindrops and mixed weather. Check each paper for its precise training and evaluation settings.
A quick thematic index to papers already listed in the main restoration or adverse-weather tables, rather than a second copy of those tables.
- Instruction and text guidance: InstructIR · InstructRestore · UniProcessor
- Vision-language priors and optimization: DACLIP-UIR · LM4LV · VLU-Net · LDR
- Agentic planning and verification: FAPE-IR · Hybrid Agents · PreActIR · SkillIR
Restoration across scientific imaging, specialist domains, video and downstream vision. Task-specific adaptation and modality limitations are noted where available.
| Method | Paper | Venue | Code |
|---|---|---|---|
| IDD-AW | IDD-AW: A Benchmark for Safe and Robust Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather | WACV 2024 | Code |
| ROD-Weather | Robust Object Detection in Challenging Weather Conditions | WACV 2024 | Code |
| Method | Paper | Venue | Code |
|---|---|---|---|
| DGCL-UIE | DGCL-UIE: Degradation-guided collaborative learning for all-in-one underwater image enhancement | Optics & Laser Technology 2026 | — |
| DIE2UIE | Underwater image enhancement via degradation information extraction and guidance | Pattern Recognition 2026 | Code |
| UniUIR | Considering Underwater Image Restoration as an All-in-One Learner | TIP 2025 | Code |
Scope: UniMIE provides cross-modality illumination/intensity enhancement. BME-X uses age- and contrast-specific checkpoints. HorusEye's additional SR, slice-thickness reconstruction, metal artifact removal and motion correction tasks require decoder fine-tuning. TAMP supports direct multi-setting CT enhancement; TAMP-S is its optionally adapted variant.
| Method | Paper | Venue | Code |
|---|---|---|---|
| UniFMIR | Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration | Nature Methods 2024 | Code |
| Cellpose3 | Cellpose3: one-click image restoration for improved cellular segmentation | Nature Methods 2025 | Code |
| FluoResFM | A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images | Nature Communications 2026 | Code |
Scope: UniFMIR combines a shared pretrained backbone with task-specific heads/tails and task/dataset fine-tuning. Cellpose3's one-click model jointly handles multiple degradation types and is optimized for downstream segmentation. FluoResFM's unified tasks are denoising, deconvolution and 2× SR; its additional 3D, projection, isotropic reconstruction and other SR-scale tasks use fine-tuning.
| Method | Paper | Venue | Code |
|---|---|---|---|
| DAMP | Degradation-Aware Metric Prompting for Hyperspectral Image Restoration | ICML 2026 | Code |
| EORestore-Agent | EORestore-Agent: Fidelity-Guided Agentic Restoration of Remote Sensing Images with Composite Degradations | arXiv 2026 | — |
| HSI-VAR | HSI-VAR: Rethinking Hyperspectral Restoration through Spatial-Spectral Visual Autoregression | arXiv 2026 | Project |
| LLaRS | A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting | arXiv 2026 | Code |
| Method | Paper | Venue | Code |
|---|---|---|---|
| DocRes | DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks | CVPR 2024 | Code |
| Method | Paper | Venue | Code |
|---|---|---|---|
| PGRDiff | All-in-one mural restoration with prompt-guided residual diffusion | npj Heritage Science 2025 | Code |
| URDiff | Learning mural restoration from degraded data via unsupervised low-rank residual diffusion | npj Heritage Science 2026 | Code |
| Method | Paper | Venue | Code |
|---|---|---|---|
| ViWS-Net | Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagation | ICCV 2023 | Code |
| DiffTTA | Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal | CVPR 2024 | Project |
| FaverNet | FaverNet: All-in-One Video Restoration via Frequency-Discriminative Conditioning | IJCV 2026 | Code |
| GDP | Generative Diffusion Prior for Unified Image and Video Restoration & Enhancement | IJCV 2026 | Code |
| MoCRA | MoCRA: Mixture of Compositional Rank-1 Atoms for 4K All-in-One Video Restoration | arXiv 2026 | — |
| ORCANet | All-in-One Video Restoration under Smoothly Evolving Unknown Weather Degradations | arXiv 2026 | Code |
| Method | Paper | Venue | Code |
|---|---|---|---|
| DUDA | Discovering Unseen Degradations to Adapt Open-World Image Restoration | NeurIPS 2026 | Code |
| RwF (domain-incremental) | Restoring Without Forgetting: Continual Learning Across Image Degradations | BMVC 2026 | Code |
| RwF (filter-level) | Restoring without Forgetting: Filter-Level Continual Image Restoration via Parameter-Space Integrated Gradients | arXiv 2026 | — |
| Method | Paper | Venue | Code |
|---|---|---|---|
| LoViF 2026 (CVPR) | LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results | CVPR Workshops 2026 | — |
| LoViF 2026 (ECCV) | The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results | ECCV Workshops 2026 | Project |
| Dataset | Usage | Download | Year |
|---|---|---|---|
| Raindrop | Train: 861/Test: set-A (58), set-B (239) | download | CVPR 2018 |
| Snow100K | 100K synthetic images | download | IEEE TIP 2018 |
| Outdoor-Rain | 9000 train/1500 val | download | CVPR 2019 |
| ALL-Weather | Train (18069): Outdoor-Rain (8250), Snow100K (9001), and Raindrop (818)/Test: Snow100K-L (16801), RaindropA (58), Outdoor-Rain (750) | download | CVPR 2022 |
| SeaIceWeather | Training,Testing | — | 2024 |
| Dataset | Usage | Download | Year |
|---|---|---|---|
| DIV2K | Training,Testing | — | 2017 |
| Flickr2K | Training | — | 2017 |
| SIDD | Real smartphone noise with estimated clean targets | Dataset | 2018 | | CDD-11 | Synthetic single and composite weather degradations | Dataset | ECCV 2024 | | FoundIR | Large-scale multi-degradation restoration; training set requires application | Project | ICCV 2025 |
Single-degradation cloud removal and dehazing benchmarks are included alongside all-in-one datasets.
| Dataset | Usage | Download | Year |
|---|---|---|---|
| SEN12MS-CR | Sentinel-1 SAR and Sentinel-2 optical; single-time cloud removal | Dataset | 2020 |
| SEN12MS-CR-TS | Sentinel-1/2 time series; multimodal cloud removal | Dataset | 2022 |
| AllClear | Sentinel-1/2 and Landsat time series; cloud removal; test set available | Dataset | NeurIPS 2024 |
| HyperDehazing | Hyperspectral dehazing; synthetic pairs and real hazy images | Dataset | ISPRS 2024 |
| MDRS-Landsat | Landsat; four degradation types for all-in-one restoration | Dataset | 2025 |
| RRSHID | Real-world paired remote sensing dehazing | Dataset | 2025 |
| MDVD-108K | UAV imagery; single and compound degradations, plus real images | Dataset | 2026 |
| Dataset | Usage | Download | Year |
|---|---|---|---|
| fastMRI | Raw knee and brain MRI data for accelerated reconstruction; application required | Apply | 2018 |
| SurgClean | Endoscopic desmoking, defogging and desplashing; adjacent clean frames are unaligned; application required | Apply | CVPR 2026 |
| Metric | Full Name | Better | Typical Use |
|---|---|---|---|
| PSNR | Peak Signal-to-Noise Ratio | ↑ | Full-reference pixel fidelity |
| SSIM | Structural Similarity Index | ↑ | Full-reference structural similarity |
| LPIPS | Learned Perceptual Image Patch Similarity | ↓ | Full-reference perceptual difference |
| NIQE | Natural Image Quality Evaluator | ↓ | No-reference perceptual quality |
| FID | Fréchet Inception Distance | ↓ | Distribution-level perceptual quality |
Metrics should be interpreted alongside the evaluation protocol and dataset; a lower perceptual distance does not necessarily imply higher pixel-wise fidelity.
- A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends — TPAMI 2025. Paper · Companion reading list
- 2026-11-09: Added DGCL-UIE and DIE2UIE to the underwater multi-degradation restoration collection.
- 2026-11-05: Expanded the dataset collection with remote sensing, medical imaging and multi-degradation restoration benchmarks.
- 2026-11-03: Refreshed the guide with an original vector cover, topic navigation, reading routes and an expanded contribution guide. Bibliography, metric definitions and screening records are unchanged.
Earlier literature and project updates · catalog last updated October 29, 2026
- 2026-10-28: Added 10 Nature Portfolio papers on medical, microscopy and cultural-heritage restoration, plus UDAIR in Research.
- 2026-10-26: Added 2026 TPAMI/TIP/IJCV, ICLR/CVPR/AAAI/NeurIPS, and agentic-restoration papers; updated confirmed publication venues, verified available code, and added LoViF challenge reports.
- 2026-10-15: Standardized paper tables, removed duplicate entries, and reviewed code links. Repository-only pages are labeled Project, not Code.
- 2026-07: ClusIR: Towards Cluster-Guided All-in-One Image Restoration was accepted by ACM MM 2026.
- 2026-03-14: ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration was published at AAAI 2026.
- 2025-10-14: UniUIR: Considering Underwater Image Restoration as an All-in-One Learner was published in IEEE TIP 2025.
- 2025-04-27: Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration was accepted by IEEE TIP 2025.
- 2024-09-29: Code and models for ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration were released.
Found a useful paper, a new implementation or a broken link? Suggest an addition or correction, or follow the contribution guide to submit a pull request.
Please include the paper title, official paper link, publication venue/year and a verified implementation URL when available. You can also contact [email protected] or [email protected].
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