用于重建非线性多模式图像的无雾化工具的比较
Rola Houhou1,2, Elsie Quansah1,2, Tobias Meyer-Zedler1,2
1Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.
Biomedical optics express
|July 27, 2023
概括
人工智能 (AI) 通过减少获得高质量的图像的时间来加速生物光子多式成像. 人工智能方法,包括一个新的incSRCNN网络,增强低质量的图像迅速捕获,提高数据质量和减少扫描时间.
科学领域:
- 生物光子学 生物光子学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 生物光子多式成像提供了深刻的生物学见解,但由于高分辨率图像的采集时间长,因此受到影响.
- 缩短测量时间对于成像细胞和组织的实际应用至关重要.
研究的目的:
- 将基于人工智能的方法与加速生物光子多式模式图像采集的标准技术进行比较.
- 评估深度学习模型在从短时间扫描中重建高质量的图像方面的有效性.
主要方法:
- 与人工智能技术 (DnCNN,Noise2Noise,MIRNet,incSRCNN) 的标准方法 (中位过器,Gerchberg-Saxton) 的比较.
- 使用头部和部多式图像,从高质量 (HQ) 图像 (8s) 中生成低质量 (LQ) 图像 (2s).
- 开发并测试了incSRCNN深度学习网络,灵感来自超分辨率卷积神经网络和初始网络.
主要成果:
- 基于人工智能的方法成功地重建了从LQ扫描中改进的图像.
- 深度学习方法在提高图像质量和减少获取时间方面显示出显著的潜力.
- 拟议的incSRCNN网络实现了与其他具有更简单架构的先进模型相匹配的性能.
结论:
- 深度学习是一种有前途的方法,可以显著减少生物光子多式成像采集时间.
- incSRCNN网络提供了一个高效和有效的解决方案,用于高质量的成像,缩短扫描时间.
- 这项研究为更快,更实用的生物光子成像应用铺平了道路.
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