用图像块提升医学影像反问题中扩散先验的效率与可行性
Investigating the Feasibility of Patch-based Inference for Generalized Diffusion Priors in Inverse Problems for Medical Images
- 采用图像块训练和推理，降低内存占用
- 在多任务多数据集上保持性能，仅需微调避免伪影
- 适合资源受限场景下的医学图像重建
基于扩散的生成先验已广泛应用于自然及医学图像的反问题求解，如图像修复与超分辨率。然而现有方法通常采用全图训练与推理，计算开销大。本文探索在磁共振成像（MRI）中使用图像块进行扩散先验的训练与推理可行性。研究了避免伪影所需的微小调整，评估了基于块的方法在性能、内存效率以及对全图训练模型的适配性方面表现，并在多种插件式方法、任务与数据集上进行了验证。
原文摘要 · Abstract (English)
Plug-and-play approaches to solving inverse problems such as restoration and super-resolution have recently benefited from Diffusion-based generative priors for natural as well as medical images. However, solutions often use the standard albeit computationally intensive route of training and inferring with the whole image on the diffusion prior. While patch-based approaches to evaluating diffusion priors in plug-and-play methods have received some interest, they remain an open area of study. In this work, we explore the feasibility of the usage of patches for training and inference of a diffusion prior on MRI images. We explore the minor adaptation necessary for artifact avoidance, the performance and the efficiency of memory usage of patch-based methods as well as the adaptability of whole image training to patch-based evaluation - evaluating across multiple plug-and-play methods, tasks and datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。