用合成数据训练网络，有效去除快速成像中的图像伪影。
Denoising of Two-Phase Optically Sectioned Structured Illumination Reconstructions Using Encoder-Decoder Networks
- 用真实伪影叠加合成图像生成训练对，解决无真实干净图像问题。
- 训练的DAE和U-Net在真实数据上显著提升图像清晰度，各擅胜场。
- 适合需要快速成像且追求高分辨率的生物显微成像研究者。
结构光照明显微技术通过投射图案光提升图像分辨率与对比度。在两相光学切片结构光照明显微（OS-SI）中，为缩短采集时间引入了残留伪影，传统去噪方法难以有效抑制。深度学习提供了替代方案，但监督训练受限于缺乏干净的光学切片真值数据。本文研究采用编码器-解码器网络进行两相OS-SI的伪影抑制，利用真实伪影场叠加在合成图像上构建合成训练对。训练了非对称去噪自编码器（DAE）和U-Net模型，并在真实OS-SI图像上进行评估。两种网络均显著改善图像清晰度，且对不同类型的伪影各有优势。结果表明，合成数据训练可实现对OS-SI图像的监督去噪，凸显编码器-解码器网络在简化重建流程中的潜力。
原文摘要 · Abstract (English)
Structured illumination (SI) enhances image resolution and contrast by projecting patterned light onto a sample. In two-phase optical-sectioning SI (OS-SI), reduced acquisition time introduces residual artifacts that conventional denoising struggles to suppress. Deep learning offers an alternative to traditional methods; however, supervised training is limited by the lack of clean, optically sectioned ground-truth data. We investigate encoder-decoder networks for artifact reduction in two-phase OS-SI, using synthetic training pairs formed by applying real artifact fields to synthetic images. An asymmetrical denoising autoencoder (DAE) and a U-Net are trained on the synthetic data, then evaluated on real OS-SI images. Both networks improve image clarity, with each excelling against different artifact types. These results demonstrate that synthetic training enables supervised denoising of OS-SI images and highlight the potential of encoder-decoder networks to streamline reconstruction workflows.
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