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ARXIV:2604.15459 · MEDICAL IMAGE DENOISING · SUBMITTED 20 APR · 20:24 UTC · FRESHNESS STALE
ARXIV:2604.15459MEDICAL IMAGE DENOISINGSUBMITTED 20 APR · 20:24 UTCFRESHNESS STALEYuxin Liu · Yiqing Dong · Wenxue Yu · Zhan Wu · Rongjun Ge · Yang Chen · +1 at arXiv
RelativeFlow is a novel flow matching framework that tames medical image denoising by learning from noisy references, outperforming existing methods on CT and MR scans.
Opportunity summary
Pain RelativeFlow is a novel flow matching framework that tames medical image denoising by learning from noisy references, outperforming existing methods on CT and MR scans.
Evidence 0 refs | 4 sources | 50% coverage
Blocker Evidence unverified
RelativeFlow is a novel flow matching framework that tames medical image denoising by learning from noisy references, outperforming existing methods on CT and MR scans. Existing simulated-supervised discriminative learning (SimSDL) and simulated-supervised generative learning…
Medical image denoising (MID) lacks absolutely clean images for supervision, leading to a noisy reference problem that fundamentally limits denoising performance. Existing simulated-supervised discriminative learning (SimSDL) and simulated-supervised generative learning (SimSGL) treat noisy references…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. RelativeFlow reformulates flow matching by decomposing the absolute noise-to-clean mapping into relative noisier-to-noisy mappings, and realizes this formulation through two key components: 1) consistent…
Medical Image Denoising moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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RelativeFlow is a novel flow matching framework that tames medical image denoising by learning from noisy references, outperforming existing methods on CT and MR scans.
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10.48550/arXiv.2604.15459RelativeFlow is a novel flow matching framework that tames medical image denoising by learning from noisy references, outperforming existing methods on CT and MR scans.
Abstract
Medical image denoising (MID) lacks absolutely clean images for supervision, leading to a noisy reference problem that fundamentally limits denoising performance. Existing simulated-supervised discriminative learning (SimSDL) and simulated-supervised generative learning (SimSGL) treat noisy references as clean targets, causing suboptimal convergence or reference-biased learning, while self-supervised learning (SSL) imposes restrictive noise assumptions that are seldom satisfied in realistic MID scenarios. We propose \textbf{RelativeFlow}, a flow matching framework that learns from heterogeneous noisy references and drives inputs from arbitrary quality levels toward a unified high-quality target. RelativeFlow reformulates flow matching by decomposing the absolute noise-to-clean mapping into relative noisier-to-noisy mappings, and realizes this formulation through two key components: 1) consistent transport (CoT), a displacement map that constrains relative flows to be components of and progressively compose a unified absolute flow, and 2) simulation-based velocity field (SVF), which constructs a learnable velocity field using modality-specific degradation operators to support different medical imaging modalities. Extensive experiments on Computed Tomography (CT) and Magnetic Resonance (MR) denoising demonstrate that RelativeFlow significantly outperforms existing methods, taming MID with noisy references.
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Dimensions overall score 7.0
PROBLEM
RelativeFlow is a novel flow matching framework that tames medical image denoising by learning from noisy references, outperforming existing methods on CT and MR scans. Existing simulated-supervised discriminative learning (SimSDL) and simulated-supervised generative learning (S...
METHOD
Medical image denoising (MID) lacks absolutely clean images for supervision, leading to a noisy reference problem that fundamentally limits denoising performance. Existing simulated-supervised discriminative learning (SimSDL) and simulated-supervised generative learning (SimSGL)...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. RelativeFlow reformulates flow matching by decomposing the absolute noise-to-clean mapping into relative noisier-to-noisy mappings, and realizes this formulation through two key components: 1) consistent...
WHY NOW
Medical Image Denoising moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
overall noise, but the fine edge details in the indicated anatomical regions are not recovered, a consequence of reference-biased learning that fails to capture subtle tissue boundaries
Implication not extracted yet.
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plicitly captures Rician noise characteristics in MR imaging and constrains the learned velocity field for robust denois- ing. SimSDL methods achieve at most 34.35 dB PSNR, with SSIM at or below 92
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rately recovers both structural integrity and fine anatomical detail from heavily degraded CT images, yielding results that surpass even the noisy reference in clarity
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recovers both indicated fine-detail regions in the brain parenchyma and produces overall sharp and structurally ac- curate denoised images. SimSDL methods (SwinIR and
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Yuxin Liu1 Yiqing Dong4 Wenxue Yu1 Zhan Wu1 Rongjun Ge3 Yang Chen1* Yuting He2* 1School of Computer Science and Engineering, Southeast University, China 2Department of Biomedical Engineering
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partial
2.9% in LPIPS, respectively. These gains come from learn- ing relative flows across CT scans with different noise lev- els
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partial
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RelativeFlow is a novel flow matching framework that tames medical image denoising by learning from noisy references, outperforming existing methods on CT and MR scans.
Segment
Medical Image Denoising
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Commercial read
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