Li Biyuan, Tang Chen, Gao Guannan, Chen Mingming, Tang Shuwei, Lei Zhenkun
Appl Opt. 2017 Jun 1;56(16):4843-4853. doi: 10.1364/AO.56.004843.
Filtering off speckle noise from a fringe image is one of the key tasks in electronic speckle pattern interferometry (ESPI). In general, ESPI fringe images can be divided into three categories: low-density fringe images, high-density fringe images, and variable-density fringe images. In this paper, we first present a general filtering method based on variational image decomposition that can filter speckle noise for ESPI fringe images with various densities. In our method, a variable-density ESPI fringe image is decomposed into low-density fringes, high-density fringes, and noise. A low-density fringe image is decomposed into low-density fringes and noise. A high-density fringe image is decomposed into high-density fringes and noise. We give some suitable function spaces to describe low-density fringes, high-density fringes, and noise, respectively. Then we construct several models and numerical algorithms for ESPI fringe images with various densities. And we investigate the performance of these models via our extensive experiments. Finally, we compare our proposed models with the windowed Fourier transform method and coherence enhancing diffusion partial differential equation filter. These two methods may be the most effective filtering methods at present. Furthermore, we use the proposed method to filter a collection of the experimentally obtained ESPI fringe images with poor quality. The experimental results demonstrate the performance of our proposed method.
从条纹图像中滤除散斑噪声是电子散斑图案干涉术（ESPI）中的关键任务之一。一般来说，ESPI条纹图像可分为三类：低密度条纹图像、高密度条纹图像和变密度条纹图像。在本文中，我们首先提出一种基于变分图像分解的通用滤波方法，该方法可以对各种密度的ESPI条纹图像滤除散斑噪声。在我们的方法中，变密度ESPI条纹图像被分解为低密度条纹、高密度条纹和噪声。低密度条纹图像被分解为低密度条纹和噪声。高密度条纹图像被分解为高密度条纹和噪声。我们分别给出一些合适的函数空间来描述低密度条纹、高密度条纹和噪声。然后我们为各种密度的ESPI条纹图像构建了几个模型和数值算法。并且我们通过大量实验研究了这些模型的性能。最后，我们将我们提出的模型与加窗傅里叶变换方法和相干增强扩散偏微分方程滤波器进行了比较。这两种方法可能是目前最有效的滤波方法。此外，我们使用所提出的方法对一组质量较差的实验获得的ESPI条纹图像进行滤波。实验结果证明了我们所提出方法的性能。
