用局部监督实现全局图像修复，少用标注也能达到全监督效果。
Locally-Supervised Global Image Restoration
- 利用图像分布的多重不变性，从局部信息推断全局内容。
- 在光声显微镜中仅需少量真实数据，就实现了媲美全监督的超分辨率效果。
- 适合标注稀缺但需高质量重建的医学成像场景。
我们研究基于学习的图像重构问题，涵盖上采样与补全两种任务。传统监督方法依赖完整采样真值数据，自监督方法虽可接受不完整真值，但通常假设采样随机且整体覆盖均匀。本文针对固定、确定性的采样模式（即使期望下也存在覆盖缺失）提出新方法。通过挖掘图像分布的多重不变性，理论上可实现与全监督相当的重建性能。我们在光声显微镜（PAM）的光学分辨率上采样任务上验证了该方法，结果表明其性能与现有方法相当或更优，且所需真实标注数据显著减少。
原文摘要 · Abstract (English)
We address the problem of image reconstruction from incomplete measurements, encompassing both upsampling and inpainting, within a learning-based framework. Conventional supervised approaches require fully sampled ground truth data, while self-supervised methods allow incomplete ground truth but typically rely on random sampling that, in expectation, covers the entire image. In contrast, we consider fixed, deterministic sampling patterns with inherently incomplete coverage, even in expectation. To overcome this limitation, we exploit multiple invariances of the underlying image distribution, which theoretically allows us to achieve the same reconstruction performance as fully supervised approaches. We validate our method on optical-resolution image upsampling in photoacoustic microscopy (PAM), demonstrating competitive or superior results while requiring substantially less ground truth data.
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