# All-In-One Medical Image Restoration via Task-Adaptive Routing

arXiv: https://arxiv.org/abs/2405.19769 · HTML: https://mlsanity.com/paper/2405.19769/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 25 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 18 of 18)

- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [PromptIR: Prompting for All-in-One Blind Image Restoration](https://mlsanity.com/paper/2306.13090/) (2023)
- [Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network](https://mlsanity.com/paper/1803.01417/) (2018)
- [Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs](https://mlsanity.com/paper/2206.04674/) (2022)
- [Towards Effective Multiple-in-One Image Restoration: A Sequential and Prompt Learning Strategy](https://mlsanity.com/paper/2401.03379/) (2024)
- [CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising](https://mlsanity.com/paper/2202.13517/) (2022)
- [Eformer: Edge Enhancement based Transformer for Medical Image Denoising](https://mlsanity.com/paper/2109.08044/) (2021)
- [Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising](https://mlsanity.com/paper/2209.03300/) (2022)
- [Low-dose CT denoising with convolutional neural network](https://mlsanity.com/paper/1610.00321/) (2016)
- [3D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesis](https://mlsanity.com/paper/2205.08887/) (2022)
- [DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image Synthesis](https://mlsanity.com/paper/2307.05249/) (2023)

## Built on by (most cited first, 30 of 30)

- [Complexity Experts are Task-Discriminative Learners for Any Image Restoration](https://mlsanity.com/paper/2411.18466/) (2024)
- [A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends](https://mlsanity.com/paper/2410.15067/) (2024)
- [Degradation-Aware Feature Perturbation for All-in-One Image Restoration](https://mlsanity.com/paper/2505.12630/) (2025)
- [Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV](https://mlsanity.com/paper/2407.11087/) (2024)
- [UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis](https://mlsanity.com/paper/2510.15710/) (2025)
- [Learning Domain-Aware Task Prompt Representations for Multi-Domain All-in-One Image Restoration](https://mlsanity.com/paper/2603.01725/) (2026)
- [Restore-R1: Efficient Image Restoration Agents via Reinforcement Learning with Multimodal LLM Perceptual Feedback](https://mlsanity.com/paper/2512.18599/) (2025)
- [TAT: Task-Adaptive Transformer for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2512.14550/) (2025)
- [A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting](https://mlsanity.com/paper/2604.05629/) (2026)
- [Artificial Intelligence for Biomedical Video Generation](https://mlsanity.com/paper/2411.07619/) (2024)
- [FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising](https://mlsanity.com/paper/2508.17299/) (2025)
- [EndoIR: Degradation-Agnostic All-in-One Endoscopic Image Restoration via Noise-Aware Routing Diffusion](https://mlsanity.com/paper/2511.05873/) (2025)
- [CLIP-driven rain perception: Adaptive deraining with pattern-aware network routing and mask-guided cross-attention](https://mlsanity.com/paper/2506.01366/) (2025)
- [UniFuse: A Unified All-in-One Framework for Multi-Modal Medical Image Fusion Under Diverse Degradations and Misalignments](https://mlsanity.com/paper/2506.22736/) (2025)
- [DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization](https://mlsanity.com/paper/2605.08627/) (2026)
- [Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation](https://mlsanity.com/paper/2501.14013/) (2025)
- [Rapid Bone Scintigraphy Enhancement via Semantic Prior Distillation from Segment Anything Model](https://mlsanity.com/paper/2503.02321/) (2025)
- [Benchmarking Endoscopic Surgical Image Restoration and Beyond](https://mlsanity.com/paper/2505.19161/) (2025)
- [Pruning Overparameterized Multi-Task Networks for Degraded Web Image Restoration](https://mlsanity.com/paper/2510.14463/) (2025)
- [WaMaIR: Image Restoration via Multiscale Wavelet Convolutions and Mamba-based Channel Modeling with Texture Enhancement](https://mlsanity.com/paper/2510.16765/) (2025)
- [Vision-Language Controlled Deep Unfolding for Joint Medical Image Restoration and Segmentation](https://mlsanity.com/paper/2601.23103/) (2026)
- [SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration](https://mlsanity.com/paper/2603.05940/) (2026)
- [MedQ-UNI: Toward Unified Medical Image Quality Assessment and Restoration via Vision-Language Modeling](https://mlsanity.com/paper/2603.18465/) (2026)
- [Mtextsuperscript4Fuse: Lightweight State-Space MoE with a Cross-Scale Gating Bridge for Brain Tumor Segmentation](https://mlsanity.com/paper/2605.02444/) (2026)
- [Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts](https://mlsanity.com/paper/2606.00146/) (2026)
- [DVANet: Degradation-aware Visual-prior Alignment Network for Image Restoration](https://mlsanity.com/paper/2606.19097/) (2026)
- [ProSAC-CT: Progressive Spectral-Anatomical Co-Guided Multi-Stage Diffusion Model for Low-Dose CT Denoising](https://mlsanity.com/paper/2607.01756/) (2026)
- [Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance](https://mlsanity.com/paper/2608.09482/) (2026)
- [MARR: Decoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2609.06645/) (2026)
- [UniH3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2609.11156/) (2026)

## Abstract (arXiv)

Although single-task medical image restoration (MedIR) has witnessed remarkable success, the limited generalizability of these methods poses a substantial obstacle to wider application. In this paper, we focus on the task of all-in-one medical image restoration, aiming to address multiple distinct MedIR tasks with a single universal model. Nonetheless, due to significant differences between different MedIR tasks, training a universal model often encounters task interference issues, where different tasks with shared parameters may conflict with each other in the gradient update direction. This task interference leads to deviation of the model update direction from the optimal path, thereby affecting the model's performance. To tackle this issue, we propose a task-adaptive routing strategy, allowing conflicting tasks to select different network paths in spatial and channel dimensions, thereby mitigating task interference. Experimental results demonstrate that our proposed All-in-one Medical Image Restoration (AMIR) network achieves state-of-the-art performance in three MedIR tasks: MRI super-resolution, CT denoising, and PET synthesis, both in single-task and all-in-one settings. The code and data will be available at hrefhttps://github.com/Yaziwel/All-In-One-Medical-Image-Restoration-via-Task-Adaptive-Routing.githttps://github.com/Yaziwel/AMIR.