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arXiv · 2609.26103

MIAR: Medical Image Super-Resolution With Autoregressive Modeling

Fang Li·Yinglong Li·Hongyu Wu·Yang Gao·Minwei Zhao·Aimin Hao
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Abstract

Medical Image Super-Resolution (MISR) aims to enhance spatial resolution without requiring hardware modifications. Although deep learning has yielded promising results, existing paradigms face a critical trade-off: diffusion-based methods suffer from prohibitive inference latency and compromised structural fidelity, whereas regression-based models typically produce over-smoothed results that lack perceptual realism. To address these limitations, we propose MIAR, which reformulates super-resolution as a conditional and progressive next-scale prediction task through a multi-scale autoregressive framework. To ensure structural fidelity, we augment the autoregressive backbone with a Scale-Adaptive Structural Decoder. Furthermore, we integrate a hierarchical beam search strategy during inference to mitigate the recursive error accumulation inherent in autoregressive generation, a phenomenon that is especially pronounced in medical images. Extensive experiments demonstrate that MIAR establishes new state-of-the-art benchmarks while maintaining superior fidelity. Notably, our framework achieves a 7.86% improvement in the perceptual metric MUSIQ compared with the state of the art, while simultaneously delivering a 2.02x speedup over diffusion-based methods.

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Fang Li, Yinglong Li, Hongyu Wu, Yang Gao, Minwei Zhao, Aimin Hao. 2026-08-05. MIAR: Medical Image Super-Resolution With Autoregressive Modeling. https://arxiv.org/abs/2609.26103

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