...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Optical engineering >Resolution and accuracy of nonlinear regression of point spread function with artificial neural networks
【24h】

Resolution and accuracy of nonlinear regression of point spread function with artificial neural networks

机译：人工神经网络的点扩散函数非线性回归的分辨率和精度

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We had already demonstrated a numerical model for the point spread function (PSF) of an optical system that can efficiently model both the experimental measurements and the lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allow for completely new ways to validate optical systems, which is especially interesting not only for mass production optics such as in the automotive industry but also for ophthalmology. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network (ANN). After briefly describing both the principle and the applications of the model, we then discuss two optically important aspects: the spatial resolution and the accuracy of the model. Using mean squared error (MSE) as a metric, we vary the topology of the neural network, both in the number of neurons and in the number of hidden layers. Measurement and simulation of a PSF can have a much higher spatial resolution than the typical pixel size used in current camera sensors. We discuss the influence this has on the topology of the ANN. The relative accuracy of the averaged pixel MSE is below 10~4, thus giving confidence that the regression does indeed model the measurement data with good accuracy. This article is only the starting point, and we propose several research avenues for future work. point spread function; modulation transfer function; image quality; sharpness; artificial neural network.
机译：我们已经演示了光学系统的点扩散函数（PSF）的数值模型，该模型可以有效地对PSF的实验测量和透镜设计仿真建模。新颖之处在于该模型的可移植性和参数化，这允许使用全新的方法来验证光学系统，这不仅对于诸如汽车行业的批量生产的光学器件而且对于眼科尤为重要。该模型的数值基础是使用人工神经网络（ANN）对PSF进行非线性回归。在简要描述了模型的原理和应用之后，我们将讨论两个光学上重要的方面：空间分辨率和模型的准确性。使用均方误差（MSE）作为度量标准，我们可以改变神经网络的拓扑结构，既可以改变神经元的数量，也可以改变隐藏层的数量。 PSF的测量和模拟可以比当前相机传感器中使用的典型像素大小具有更高的空间分辨率。我们讨论了这对ANN拓扑的影响。平均像素MSE的相对精度低于10〜4，因此可以确信回归确实可以以良好的精度对测量数据进行建模。本文仅作为起点，我们为以后的工作提出了几种研究途径。点扩散功能调制传递函数；画面质量;锐度人工神经网络。

著录项

  • 来源
    《Optical engineering》 |2019年第4期|045101.1-045101.9|共9页
  • 作者

    Matthias Lehmann; Christian Wittpahl; Hatem Ben Zakour; Alexander Braun;

  • 作者单位

    Dusseldorf University of Applied Sciences, Dusseldorf, Germany;

    Dusseldorf University of Applied Sciences, Dusseldorf, Germany;

    Dusseldorf University of Applied Sciences, Dusseldorf, Germany;

    Dusseldorf University of Applied Sciences, Dusseldorf, Germany;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号