UniH$^3$: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration
Updated 1 h ago · first seen 11 Sept 2026
paper_01M294H1TZ888GG2SDGCAZ7AKE
- Published
- 11 Sept 2026
- T1 · 3 h ago
- arXiv
- 2609.11156
- T1 · 3 h ago
- Category
- cs.CV
- T1 · 3 h ago
Abstract
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 (e.g., distinct data distributions and degradation types). However, they largely neglect the inherent homogeneity present in medical images, such as widely shared anatomical structures within and across modalities, which can be leveraged to ease model training and improve generalization. To this end, we propose UniH3, a novel framework that Unifies Hierarchical Homogeneity and Heterogeneity for all-in-one medical image restoration. Specifically, to comprehensively exploit homogeneity, we introduce a Hierarchical Homogeneity Memory (H2M) module that progressively distills intra- and inter-task homogeneity priors from high-quality images during training, and adaptively retrieves the most relevant priors tailored to the input for guided restoration. These retrieved priors are then injected into the restoration pipeline via an efficient Homogeneity-Guided Attention (HGA) mechanism. Furthermore, to comprehensively address heterogeneity, we design a Hierarchical Heterogeneity Balancer (H2B) that mitigates both inter- and intra-task conflicts during optimization, facilitating balanced and effective multi-task learning. Extensive experiments on two large-scale benchmarks, MedIR-2D-500K and MedIR-3D-3K, demonstrate that UniH3 achieves state-of-the-art performance on both all-in-one and single-task medical image restoration. We hope this work establishes a strong benchmark and advances the development of general-purpose medical image restoration models. Code is available at https://github.com/Yaziwel/UniH3.
Authors 6
Zhiwen Yang, Jiayin Li, Chengyu Liu, Hui Zhang, Bingzheng Wei, Yan Xu
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- Official page
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
- Arxiv announce type
- new
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
- arXiv id
- 2609.11156
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
- Categories
- cs.CV
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
- Github repo
- Yaziwel/UniH3
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 3 h agomedium
- Hf paper url
- https://huggingface.co/papers/2609.11156
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 3 h agomedium
- Github stars
- 5
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- 1
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- Upvotes
- 2
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 1 h agomedium
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
- Primary category
- cs.CV
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
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- Authors
- Zhiwen Yang, Jiayin Li, Chengyu Liu
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| Value | Valid from → to | Status | Source | Confidence | Extractor |
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- New paperPaperUniH$^3$: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration
New paper: UniH$^3$: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.CV | feed | T1· Official | 2 h ago | 1 |
| Hugging Face Hub (public pages, model cards, papers) | huggingface.co/papers | listing | T2· Quality secondary | 1 h ago | 3 |
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