DBDNet for denoising in ESPI wrapped phase patterns with high density and high speckle noise.

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Abstract

In this paper, we propose a dilated-blocks-based deep convolution neural network, named DBDNet, for denoising in electronic speckle pattern interferometry (ESPI) wrapped phase patterns with high density and high speckle noise. In our method, the proposed dilated blocks have a specific sequence of dilation rate and a multilayer cascading fusion structure, which can better improve the effect of speckle noise reduction, especially for phase patterns with high noise and high density. Furthermore, we have built an abundant training dataset with varieties of densities and noise levels to train our network; thus, the trained model has a good generalization and can denoise ESPI wrapped phase in various circumstances. The network can get denoised results directly and does not need any pre-process or post-process. We test our method on one group of computer-simulated ESPI phase patterns and one group of experimentally obtained ESPI phase patterns. The test images have a high degree of speckle noise and different densities. We compare our method with two representative methods in the spatial domain and frequency domain, named oriented-couple partial differential equation and windowed Fourier low pass filter (LPF), and a method based on deep learning, named fast and flexible denoising convolutional neural network (FFDNet). The denoising performance is evaluated quantitatively and qualitatively. The results demonstrate that our method can reduce high speckle noise and restore the dense areas of ESPI phase patterns, and get better results than the compared methods. We also apply our method to a series of phase patterns from a dynamic measurement and get successful results.