UniRestorer++: Scalable Universal Image Restoration with Multi-Granularity Degradation Modeling
UniRestorer++ is a framework for universal image restoration that handles diverse degradations in one model.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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UniRestorer++ is a framework for universal image restoration that handles diverse degradations in one model. It uses multi-granularity degradation modeling, group-specific experts, and routing based on degradation and granularity estimation, plus region-aware routing for spatially non-uniform corruptions. The authors introduce CDD-50, a benchmark with 50 degradation categories. They report approximately log-linear PSNR trends over the evaluated model-size range, stronger scaling gains than vanilla MoE on out-of-distribution and real-world data, and state-of-the-art results with zero-shot and few-shot transfer.
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The authors report approximately log-linear PSNR trends across the evaluated model-size range.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseScaling gains are reported as stronger than vanilla MoE on out-of-distribution and real-world data.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseCDD-50 is a benchmark covering 50 degradation categories, from single to penta-degradation.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseThe paper reports state-of-the-art performance and zero-shot and few-shot transfer on real-world images.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
Universal restoration models that remain robust to unseen and real-world degradations could reduce the need for task-specific restoration pipelines. The paper's scaling analysis suggests specialization can grow with capacity, though this depends on the reported evaluations.
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