Diffusion models have been increasingly used as strong generative priors for solving inverse problems such as super-resolution in medical imaging. However, these approaches typically utilize a diffusion prior trained at a single scale, ignoring the hierarchical scale structure of image data. In this work, we propose to decompose images into Laplacian pyramid scales and train separate diffusion priors for each frequency band. We then develop an algorithm to perform super-resolution that utilizes these priors to progressively refine reconstructions across different scales. Evaluated on brain, knee, and prostate MRI data, our approach both improves perceptual quality over baselines and reduces inference time through smaller coarse-scale networks. Our framework unifies multiscale reconstruction and diffusion priors for medical image super-resolution.

翻译：扩散模型作为强大的生成先验，在解决医学影像超分辨率等逆问题中的应用日益广泛。然而，现有方法通常仅利用单一尺度训练的扩散先验，忽略了图像数据的层次化尺度结构。本研究提出将图像分解为拉普拉斯金字塔尺度，并为每个频带训练独立的扩散先验。随后开发了一种超分辨率算法，利用这些先验在不同尺度上逐步优化重建结果。通过在脑部、膝关节和前列腺MRI数据上的评估，本方法在提升基线模型感知质量的同时，通过更精简的粗尺度网络减少了推理时间。该框架将多尺度重建与扩散先验相统一，实现了医学图像超分辨率的创新。