深度学习对修改的泽尼克系数的估计和在流中恢复点传播函数的估计
Optics express
|July 21, 2023
概括
这项研究使用深度学习模型从单个图像中恢复流降级的点分布函数. 卷积神经网络预测了泽尼克系数,使波面偏差纠正能够改善成像.
科学领域:
- 光学成像技术的使用.
- 波浪前线传感 波浪前线传感
- 计算光学是一种计算光学.
背景情况:
- 流会显著降低图像质量,因为它会引发波面偏差.
- 精确的点扩散函数 (PSF) 恢复对于各种成像应用至关重要.
- 现有的偏差校正方法往往需要复杂的设置或多次测量.
研究的目的:
- 开发一种深度学习方法,从单一强度图像中恢复流降解的PSF.
- 预测修改的泽尼克多项式系数,表示波面偏差.
- 评估模型在不同流和噪声条件下的性能.
主要方法:
- 使用卷积神经网络 (CNN) 来分析强度图像.
- 美国有线电视新闻网 (CNN) 接受了对点和扩展物体的模拟数据的训练.
- 该模型预测了修改后的泽尼克系数,将绝对值分配给偶数的辐射顺序,以解决符号模两可.
主要成果:
- 深度学习模型成功预测了修改后的泽尼克系数.
- 在成像一个点对象时,可以达到最佳的预测准确度.
- 即使对于带有噪音和流的扩展物体,也证明了有用的系数回收.
结论:
- 深度学习提供了一种可行的方法,可以从单一强度图像中恢复流引起的波浪偏差.
- 在具有挑战性的成像环境中,CNN模型为PSF估计提供了实用的方法.
- 这种技术在适应光学和天文成像中具有潜在的应用.
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