Tahon Marie, Montrésor Silvio, Picart Pascal
LIUM (Laboratoire d'Informatique de l'Université du Mans), Le Mans Université, Avenue Olivier Messiaen, 72085 Le Mans, France.
LAUM (Laboratory of Acoustics of Le Mans Université), CNRS 6613, Institut d'Acoustique-Graduate School (IA-GS), Le Mans Université, Avenue Olivier Messiaen, 72085 Le Mans, France.
J Imaging. 2022 Jun 9;8(6):165. doi: 10.3390/jimaging8060165.
Digital holography is well adapted to measure any modifications related to any objects. The method refers to digital holographic interferometry where the phase change between two states of the object is of interest. However, the phase images are corrupted by the speckle decorrelation noise. In this paper, we address the question of de-noising in holographic interferometry when phase data are polluted with speckle noise. We present a new database of phase fringe images for the evaluation of de-noising algorithms in digital holography. In this database, the simulated phase maps present characteristics such as the size of the speckle grains and the noise level of the fringes, which can be controlled by the generation process. Deep neural network architectures are trained with sets of phase maps having differentiated parameters according to the features. The performances of the new models are evaluated with a set of test fringe patterns whose characteristics are representative of severe conditions in terms of input SNR and speckle grain size. For this, four metrics are considered, which are the PSNR, the phase error, the perceived quality index and the peak-to-valley ratio. Results demonstrate that the models trained with phase maps with a diversity of noise characteristics lead to improving their efficiency, their robustness and their generality on phase maps with severe noise.
数字全息术非常适合测量与任何物体相关的任何变化。该方法涉及数字全息干涉测量法，其中关注的是物体两个状态之间的相位变化。然而，相位图像会受到散斑去相关噪声的干扰。在本文中，我们探讨了在相位数据被散斑噪声污染时全息干涉测量中的去噪问题。我们提出了一个新的相位条纹图像数据库，用于评估数字全息术中的去噪算法。在这个数据库中，模拟的相位图呈现出诸如散斑颗粒大小和条纹噪声水平等特征，这些特征可以通过生成过程进行控制。深度神经网络架构使用根据特征具有不同参数的相位图集合进行训练。新模型的性能通过一组测试条纹图案进行评估，这些图案的特征在输入信噪比和散斑颗粒大小方面代表了恶劣条件。为此，考虑了四个指标，即峰值信噪比、相位误差、感知质量指数和峰谷比。结果表明，使用具有多种噪声特征的相位图训练的模型能够提高它们在具有严重噪声的相位图上的效率、鲁棒性和通用性。
