Robust ESPI fringe pattern denoising method using the Restormer Partial Differential Network (Res-PDNet).

Abstract

Electronic speckle pattern interferometry (ESPI) is an important non-destructive testing technique. Denoising the interference fringe pattern is the key link of this technique as well as a hot spot of current research. In this work, we introduce the Restormer Partial Differential Network (Res-PDNet) for ESPI fringe denoising. The method uses PDNet as the base network, which combines the partial differential equation denoising idea and deep learning model, and optimizes the network according to the characteristics of the fringe patterns. In order to balance fringe denoising and structure preservation, our approach incorporates two key enhancements. First, Multi-Dconv Head Transposed Attention and Gated-Dconv Feed-Forward Network modules of Restormer are added after the residual blocks of PDNet, which allows the network to capture more information about the fringe structure and texture. Second, orientation constraints of the fringe pattern are introduced in the loss function to further protect the fringe shape. The Res-PDNet network can accurately recognize the fringe structure and maintain the fringe shape while filtering out noise. It has a good denoising effect on the electron scattering interference fringe pattern.