从一个封闭的边缘模式中进行一步准确的相模解调,使用卷积神经网络HRUnet
Applied optics
|March 4, 2024
概括
一个新的卷积神经网络 (CNN),HRUnet,准确地从光学干涉测量的单个封闭边缘模式中检索相位图. 这种方法超越了现有的CNN,用于精确的相位调节.
科学领域:
- 光学干涉测量是一种光学干涉测量.
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 从单个封闭边缘模式中检索相位图是光学干涉测量的重大挑战.
- 现有的方法往往在准确性和效率方面扎.
研究的目的:
- 提出一种新的卷积神经网络 (CNN),HRUnet,用于从封闭边缘模式中准确的相变调.
- 通过模拟和真实边缘模式数据来证明HRUnet的有效性.
主要方法:
- 开发了HRUnet,这是从Unet模型衍生的CNN,包含一个高分辨率网络 (HRnet) 模块和剩余块.
- 训练网络从缩放的边缘模式直接输出未包装的相位图.
- 将HRUnet的表现与其他两家CNN进行了比较.
主要成果:
- HRUnet成功地从模拟和实际边缘模式中高精度地调节了相位.
- 拟议的HRUnet与其他两个当代CNN模型相比,显示出更高的准确性.
- 该网络有效地提取高分辨率的特征地图,并减轻梯度消失.
结论:
- HRUnet提供了一个高度准确和有效的解决方案,用于从单个封闭边缘模式中检索相位图.
- 集成HRnet模块和剩余块显著提高了CNN在该应用程序中的性能.
- 这种方法推进了光学干涉测量的相变调技术.
相关概念视频
Linear Approximation in Frequency Domain
89
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
89
Time and frequency -Domain Interpretation of Phase-lead Control
84
Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
84
Time and frequency -Domain Interpretation of Phase-lag Control
92
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
92
Reconstruction of Signal using Interpolation
195
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
195
Downsampling
157
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
157
Linear Approximation in Time Domain
81
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
81


