MIAR：基于自回归建模的医学图像超分辨率
MIAR: Medical Image Super-Resolution With Autoregressive Modeling
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中文总结 AI 辅助
MIAR通过多尺度自回归框架将医学图像超分辨率重构为渐进式下一尺度预测，并引入尺度自适应结构解码器与层次化束搜索，在MUSIQ上提升7.86%且比扩散方法快2.02倍。
中文摘要 AI 辅助
医学图像超分辨率（MISR）旨在无需硬件修改的情况下提升空间分辨率。尽管深度学习已取得令人鼓舞的成果，现有范式仍面临关键权衡：基于扩散的方法存在推理延迟过高和结构保真度受损的问题，而基于回归的模型通常产生过度平滑、缺乏感知真实感的结果。为解决这些局限，我们提出MIAR，通过多尺度自回归框架将超分辨率重构为条件式、渐进式的下一尺度预测任务。为确保结构保真度，我们为自回归主干网络增补了尺度自适应结构解码器。此外，我们在推理阶段集成层次化束搜索策略，以缓解自回归生成中固有的递归误差累积问题，该现象在医学图像中尤为显著。大量实验表明，MIAR在保持优越保真度的同时，确立了新的最先进基准。值得注意的是，与现有最先进方法相比，我们的框架在感知指标MUSIQ上实现了7.86%的提升，同时相比基于扩散的方法实现了2.02倍的加速。
英文摘要
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.
