用近似分解加速无监督图像重建，训练速度提升10倍
Fast Equivariant Imaging: Accelerating Unsupervised Learning and Model Adaptation via Inexact Splitting
- 通过近似变量分解解耦网络训练与去噪修复步骤
- 在CT重建和图像修复上实现10倍加速且泛化性能更好
- 适合需要快速适配单张图像的无监督场景
本文提出快速等变成像（FEI），一种新型无监督学习框架，可在无需真实标签数据的情况下快速高效训练深度成像网络。FEI通过非精确变量分裂方案重构等变成像目标，将网络训练与基于即插即用去噪器的辅助修复步骤解耦，相比标准等变成像范式展现出更优的效率与性能。具体而言，该方法在训练U-Net进行X射线CT重建和图像修复任务时，相较标准EI实现数量级（10倍）加速，同时提升泛化能力。除离线训练外，该方案还支持预训练模型在测试时对单个样本的高效适应，进一步提升性能。大量实验表明，所提方法在效率与性能上显著优于现有无监督方法及模型适配技术。
原文摘要 · Abstract (English)
In this work, we propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to rapidly and efficiently train deep imaging networks without ground-truth data. FEI reformulates the EI objective through an inexact variable-splitting scheme, decoupling network training from an auxiliary restoration step implemented with a plug-and-play denoiser, this novel unsupervised scheme shows superior efficiency and performance compared to the standard Equivariant Imaging paradigm. In particular, our FEI schemes achieve an order-of-magnitude (10x) acceleration over standard EI on training U-Net for X-ray CT reconstruction and image inpainting, with improved generalization performance. Beyond offline training, the proposed scheme also enables efficient test-time adaptation of a pretrained model to individual samples, to secure further performance improvements. Extensive experiments show that the proposed approach provides a noticeable efficiency and performance gain over existing unsupervised methods and model adaptation techniques.
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