Image Restoration
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Benchmarks
Most implemented
Noise2Noise: Learning Image Restoration without Clean Data
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
Deep Image Prior
Simple Baselines for Image Restoration
Restormer: Efficient Transformer for High-Resolution Image Restoration
Learning Enriched Features for Real Image Restoration and Enhancement
Papers
Learning from Failure: Leveraging Unreliable Predictions in Semi-Supervised Real-World Adverse Weather Removal
Adverse weather image restoration aims to recover images degraded by rain, haze, snow, and other weather-induced artifacts, thereby improving the robustness of outdoor vision systems. Existing unified restoration models …
Contrastive LearningImage RestorationConvergent Plug-and-Play Image Restoration with Annealed Noise Levels
Plug-and-Play (PnP) methods solve imaging inverse problems by incorporating deep denoisers into iterative optimization algorithms. Although practical implementations often decrease the denoiser noise level $σ$ along iter…
Image RestorationGeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observations
Cloud removal methods are typically specialized to individual datasets and input configurations, limiting reuse across sensors, spectral bands, and observation settings. We introduce GeoCR, a generalist model that unifie…
Image RestorationCloud RemovalImIR: Image-Instruction Tuning for All-in-One Image Restoration
Degradations vary widely across images, so a practical restoration system has to handle many degradation types with one model. A recent and effective recipe adapts a large pretrained image-editing model to restoration us…
Image RestorationGraLoD: Graphics-Inspired Continuous Level-of-Detail Learning for Image Restoration
The spatial support required for image restoration varies across degradation types, image regions, and reconstruction stages. However, most existing methods rely on predefined multi-scale hierarchies and aggregate featur…
Image RestorationUniH^3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration
All-in-One medical image restoration (MedIR) aims to address diverse tasks across modalities and degradation types using a single universal model. Existing methods typically prioritize modeling inter-task heterogeneity (…
Multi-Task LearningImage Restoration