The denoising of electronic speckle pattern interferometry (ESPI) fringe patterns is a key step in the application of ESPI. In this paper, we propose a method for batch denoising of ESPI fringe patterns based on a convolution neural network (CNN). In the proposed method, the network is first trained by our training dataset, which consists of the noisy ESPI fringe patterns and the corresponding noise-free images. We propose a new computer-simulated method of ESPI fringe patterns to create our training dataset. After training, the other multi-frame ESPI fringe patterns to be processed are fed to the trained network simultaneously, and the corresponding denoising images can be obtained in batches. We demonstrate the performance of the proposed method via application to 50 computer-simulated ESPI fringe patterns and three groups of experimentally obtained ESPI fringe patterns. The experimental results show that our method can obtain desired results even when the quality of ESPI fringe images is considerably low because of variable density, high noise, and low contrast, and our method can denoise multi-frame fringe patterns simultaneously. Moreover, we use the computer-simulated ESPI fringe patterns to train the network; after training, the trained network can be used to denoise either computer-simulated ESPI fringe patterns or the experimentally obtained ESPI fringe patterns. The proposed method is especially suitable for processing a large number of ESPI fringe patterns.

Batch denoising of ESPI fringe patterns based on convolutional neural network.

Optical measurement techniques – A push for digitization

Strain field estimation based on digital image correlation and radial basis function

Vibration measurement based on electronic speckle pattern interferometry and radial basis function

Comparison on performance of some representative and recent filtering methods in electronic speckle pattern interferometry

This article is concerned with frequency filtering for electronic speckle pattern interferometry wrapped phase patterns. We propose a robust localized Fourier transform filter which is an extension of the root filtering method (RFM). We improve the RFM from a simple technical process and a filter function in the frequency domain. In our method, the proposed filter function is taken as the power spectrum of the convolution of an image and a Gaussian function to the power α. We test the proposed method on two computer-simulated wrapped phase fringe patterns and one experimentally obtained wrapped phase pattern, and compare our models with the widely used, well-known RFM and windowed Fourier filtering (WFF). The experimental results have demonstrated that our localized Fourier transform filter outperforms the RFM and is comparable to WFF. Our method depends on fewer parameters, as compared with WFF, and can achieve a better balance between the computational complexity and the filtered results.

Localized Fourier transform filter for noise removal in electronic speckle pattern interferometry wrapped phase patterns

The digital speckle correlation method (DSCM) has been widely used to resolve displacement and deformation gradient fields. The computational time and the computational accuracy are still two challenging problems faced in this area. In this paper, we introduce the radial basis function (RBF) interpolation method to DSCM and propose a method for displacement field analysis based on the combination of DSCM with RBF interpolation. We test the proposed method on two computer-simulated and two experimentally obtained deformation measurements and compare it with the widely used Newton–Raphson iteration (NR method). The experimental results demonstrate that our method performs better than the NR method in terms of both quantitative evaluation and visual quality. In addition, the total computational time of our method is considerably shorter than that of the NR method. Our method is particularly suitable for displacement field analysis of large regions.

Cancan Li

Papers

Localized Fourier transform filter for noise removal in electronic speckle pattern interferometry wrapped phase patterns

Comparison on performance of some representative and recent filtering methods in electronic speckle pattern interferometry

Displacement field analysis based on the combination digital speckle correlation method with radial basis function interpolation