鉴别器
散斑噪声
计算机科学
降噪
人工智能
斑点图案
全息干涉法
电子散斑干涉技术
数字全息术
噪音(视频)
全息术
深度学习
模式识别(心理学)
光学
图像(数学)
物理
电信
探测器
作者
Qiang Fang,Haiting Xia,Qinghe Song,Meijuan Zhang,Rongxin Guo,Silvio Montrésor,Pascal Picart
出处
期刊:Optics Express
[Optica Publishing Group]
日期:2022-05-12
卷期号:30 (12): 20666-20666
被引量:23
摘要
Speckle denoising can improve digital holographic interferometry phase measurements but may affect experimental accuracy. A deep-learning-based speckle denoising algorithm is developed using a conditional generative adversarial network. Two subnetworks, namely discriminator and generator networks, which refer to the U-Net and DenseNet layer structures are used to supervise network learning quality and denoising. Datasets obtained from speckle simulations are shown to provide improved noise feature extraction. The loss function is designed by considering the peak signal-to-noise ratio parameters to improve efficiency and accuracy. The proposed method thus shows better performance than other denoising algorithms for processing experimental strain data from digital holography.
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