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基于图像指标的多观测单步深度决定性的政策梯度,用于无传感器自适应光学

Guozheng Xu¹, Thomas J Smart², Eduard Durech³

¹Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom.

Biomedical optics express

|September 30, 2024

概括

无传感器自适应光学 (SAO) 使用一种新的多观察单步深确定性政策梯度 (MOSS-DDPG) 框架,快速纠正临床前视网膜成像中的偏差. 这种方法实现了衍射有限的分辨率,与传统方法相比,代次数显著减少.

科学领域:

生物医学光学生物医学光学
计算机成像成像技术
眼科医生眼科眼科

背景情况:

无传感器自适应光学 (SAO) 对于改善各种成像模式中的图像质量至关重要.
深度决定性政策梯度 (DDPG) 与Zernike模式登 (ZMHC) 相比,已经显示出更快的SAO的承诺.
临床前视网膜成像需要精确的偏差校正,以实现高分辨率可视化.

研究的目的:

为SAO引入一个多观察单步DDPG (MOSS-DDPG) 优化框架.
将MOSS-DDPG应用于对焦扫描激光眼镜 (SLO),用于临床前视网膜成像.
在速度和准确性方面评估MOSS-DDPG的性能.

主要方法:

开发了一个MOSS-DDPG框架,利用2N+1图像清晰度度度量观测优化N泽尼克系数.
实现了MOSS-DDPG与长短期内存 (LSTM) 网络.
在模拟和在现场测试中对共聚焦SLO系统进行了模拟.

主要成果:

在模拟中,MOSS-DDPG实现了衍射限制分辨率.
转移学习使得模拟学习知识能够快速适应现实世界的系统缺陷.
在现场测试显示性能与ZMHC相比,代减少了十倍以上.

结论:

在临床前视网膜成像中,MOSS-DDPG为SAO提供了一种高效的方法.
该框架在现实条件下显示了快速的融合和强的表现.
对于高分辨率视网膜成像应用来说,MOSS-DDPG代表了一项重大进步.

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