MARR: Decoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration

Haobin Chen; Ao Chang; Heqin Zhu; Rundong Wang; Ting Liu; Shaohua Kevin Zhou

cs.CVSep 6, 2026

MARR: Decoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration

Authors: Haobin Chen, Ao Chang, Heqin Zhu, Rundong Wang, Ting Liu, Shaohua Kevin Zhou

Organizations: School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei, Anhui, 230026, China · Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE) Lab, YRD-RIGHT, Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, 215123, China · Biomedical Basic Research Center (BBRC) of Jiangsu, Suzhou, Jiangsu, 215123, China · Jiangsu Key Laboratory of Multimodal Digital Twin Technology, Suzhou, Jiangsu, 215123, China ·

Abstract

All-in-one medical image restoration seeks to recover heterogeneous clinical images with a single model, but PET, CT, and MRI differ substantially in degradation statistics, anatomical contrast, and output-space bias. A fully shared network can entangle modality-specific residual errors, whereas separate modality-specific networks sacrifice the practical advantages of unified deployment. We therefore recast all-in-one restoration as a question of where limited adaptation should be placed: policy selection, feature execution, or output calibration. We propose MARR, a compact restoration framework that constrains multi-modality adaptation into degradation-aware policy routing, modality-private residual execution, and image-domain residual correction without requiring degradation labels or separate modality-specific models. The policy branch forms a routing prompt from input statistics, latent content, and modality identity, and uses it only as a control signal. Prompt-gated modality-private adapters then perform lightweight residual refinement at intermediate decoder stages, while zero-initialized modality-specific output heads calibrate the final image-domain residual without perturbing the initial shared prediction. On an all-in-one PET, CT, and MRI restoration benchmark, MARR outperforms thirteen methods re-trained under the same protocol, achieving PSNR values of 37.34 dB, 33.85 dB, and 32.09 dB on PET, CT, and MRI, respectively, and the best modality-average PSNR of 34.43 dB. The code is publicly available at https://github.com/CHB-learner/MARR.

Figures & tables

Fig. 1: Overall architecture of MARR. The framework separates all-in-one restoration into three roles: degradation-aware prompt policy estimation for decision making, prompt-gated modality-private adaptation for execution, and zero-initialized output calibration for correction. Solid arrows denote restoration data flow and dashed arrows denote prompt/control flow.

Fig. 2: Prompt-gated modality-private adapter. The policy prompt $z_{p}$ predicts a modality gate that controls a lightweight DWConv-GELU-PWConv residual branch for the current decoder feature $F$ .

Method	PET Synthesis			CT Denoising			MRI Super-Resolution			Avg.
Method	PSNR $\uparrow$	SSIM $\uparrow$	RMSE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	RMSE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	RMSE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	RMSE $\downarrow$
NAFNet	35.15 *	0.9259 *	0.1105 *	33.39 *	0.9164 *	8.7455 *	29.83 *	0.9098 *	37.3700 *	32.79 *	0.9173 *	15.4086 *
Restormer	35.36 *	0.9276 *	0.1080 *	33.54 *	0.9177 *	8.5975 *	30.09 *	0.9136 *	36.3138 *	33.00 *	0.9197 *	15.0064 *
SwinIR	35.89 *	0.9318 *	0.1009 *	33.47 *	0.9161 *	8.6713 *	29.85 *	0.9107 *	37.1287 *	33.07 *	0.9195 *	15.3003 *
DaPT	35.53 *	0.9288 *	0.1051 *	33.45 *	0.9161 *	8.6889 *	30.08 *	0.9134 *	36.3602 *	33.02 *	0.9194 *	15.0514 *
RAT	35.46 *	0.9284 *	0.1071 *	33.52 *	0.9174 *	8.6280 *	29.92 *	0.9129 *	36.9398 *	32.96 *	0.9196 *	15.2250 *

TABLE I: All-in-One medical image restoration results. Avg. denotes the arithmetic mean over PET, CT, and MRI.

Group	Variant	Params (M)	PET Synthesis			CT Denoising			MRI Super-Resolution
Group	Variant	Params (M)	PSNR $\uparrow$	SSIM $\uparrow$	RMSE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	RMSE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	RMSE $\downarrow$
Core modules	Output calibration only	39.67	37.2644	0.9482	0.0857	33.7453	0.9185	8.4099	32.0037	0.9392	29.1782
	Private adapter only	39.75	37.2558	0.9481	0.0858	33.7476	0.9185	8.4080	32.0156	0.9394	29.1335
	MARR	39.75	37.3400	0.9485	0.0849	33.8500	0.9199	8.3469	32.0900	0.9399	29.0116
Extended designs	+ frequency adapter	39.81	37.2405	0.9481	0.0861	33.7436	0.9184	8.4114	32.0075	0.9393	29.1621
	+ frequency prompt pool	39.70	37.2498	0.9481	0.0859	33.7406	0.9185	8.4140	32.0167	0.9394	29.1392

TABLE II: Ablation study results of MARR. Bold indicates the best result in each metric column.

Fig. 3: Visual comparisons on all-in-one medical image restoration. Each row shows LQ input, representative comparison methods, MARR, and HQ target with matched zoom-in regions.

Fig. 4: Mechanism diagnostics of MARR. (A) Policy prompt embeddings visualized by t-SNE form modality-aware clusters. (B) Adapter residual norms show stronger level-2 feature correction for CT and MRI. (C) Output calibration shifts are largest for PET, indicating complementary image-domain residual correction.

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UniH $^3$ : Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

Zhiwen Yang, Jiayin Li, Chengyu Liu, Hui Zhang+2

School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China · Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China · Independent Researcher

Multi-Task Learning All-in-One Image Restoration

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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.

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All-in-One Image Restoration

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