Montresor Silvio, Tahon Marie, Picart Pascal
J Opt Soc Am A Opt Image Sci Vis. 2022 Feb 1;39(2):A62-A78. doi: 10.1364/JOSAA.444951.
We present a review of deep learning algorithms dedicated to the processing of speckle noise in coherent imaging. We focus on methods that specifically process de-noising of input images. Four main classes of applications are described in this review: optical coherence tomography, synthetic aperture radar imaging, digital holography amplitude imaging, and fringe pattern analysis. We then present deep learning approaches recently developed in our group that rely on the retraining of residual convolutional neural network structures to process decorrelation phase noise. The paper ends with the presentation of a new approach that uses an iterative scheme controlled by an input SNR estimator associated with a phase-shifting procedure.
我们对致力于处理相干成像中散斑噪声的深度学习算法进行了综述。我们重点关注专门处理输入图像去噪的方法。本综述描述了四类主要应用：光学相干断层扫描、合成孔径雷达成像、数字全息振幅成像和条纹图案分析。然后，我们介绍了我们小组最近开发的深度学习方法，该方法依赖于对残差卷积神经网络结构进行再训练以处理去相关相位噪声。本文最后介绍了一种新方法，该方法使用由与相移过程相关联的输入信噪比估计器控制的迭代方案。
