arxiv:2609.11156
Published on Sep 10
· Submitted by
Zhiwen Yang on Sep 11
Upvote
23
Authors:
,
,
,
,
,
Abstract
UniH3 unifies hierarchical homogeneity and heterogeneity for universal medical image restoration via memory-based homogeneity priors and a heterogeneity balancer.
Generated by thinkingmachines/Inkling-Small
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.
View arXiv page View PDF Project pageGitHub 7 Add to collection
Community
Paper submitter 12 days ago
11 days ago
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
-
Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance (2026)
-
MARR: Decoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration (2026)
-
DPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration (2026)
-
Degradation-Aware Prompt Learning With Cross-Modal Compensation for Adverse Weather Removal (2026)
-
MIND: Multimodal Intent-Driven Network via Diffusion Transformers for Medical Image Fusion (2026)
-
UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation (2026)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on HF Mirror checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @librarian-bot recommend
Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.
Tap or paste here to upload images
· Sign up or log in to comment
Upvote
23
Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/2609.11156 in a model README.md to link it from this page.
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/2609.11156 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/2609.11156 in a Space README.md to link it from this page.
Collections including this paper 0
No Collection including this paper
Add this paper to a collection to link it from this page.