Generalized denoising network LGCT-Net for various types of ESPI wrapped phase patterns

Abstract

In this paper, we propose a generalized network based on our proposed Local-Global Channel Transformer (LGCT) module for denoising various types of ESPI wrapped phase patterns (including low-density, medium-density, high-density, variable-density, and discontinuous phase patterns). The Conv + BN + ReLU layer consists of convolution (Conv), batch normalization (BN), and the rectified linear unit (Relu) in series. The generalized network LGCT-Net interleaves four LGCT modules with five Conv + BN + ReLU layers in a dense connection manner. We propose the LGCT Module by stacking three Dilated-Group Convolution blocks (DGC block), a Contextual Transformer block (CoT block), and an Efficient Channel Attention block (ECA block). The LGCT module simultaneously leverages the local context extraction capability of convolutions and the powerful global information extraction capability of a transformer. Additionally, it performs feature extraction in both spatial and channel dimensions. We also create a diverse ESPI wrapped phase pattern denoising dataset with various densities, shapes, noise levels, and discontinuity. We successfully train the LGCT-Net without any preprocessing or postprocessing steps. We evaluate the performance of our method on simulated and experimental ESPI wrapped phase patterns with discontinuity and different densities. Then we compare it with previously published denoising methods PEARLS, HDCNN, ADCNN, and DBDNet quantitatively and qualitatively. The results show that our method facilitates the reduction of speckle noise and the enhancement of fine details while preserving structure and shape, outperforming the compared methods. In the end, we apply our method to dynamic measurements of nuclear graphite ESPI phase patterns at different times. And then performing phase unwrapping on the filtered phase patterns, we achieve successful results.