Unsupervised speckle denoising in digital holographic interferometry based on 4-f optical simulation integrated cycle-consistent generative adversarial network

Abstract

The speckle noise generated during digital holographic interferometry (DHI) is unavoidable and difficult to eliminate, thus reducing its accuracy. We propose a self-supervised deep-learning speckle denoising method using a cycle-consistent generative adversarial network to mitigate the effect of speckle noise. The proposed method integrates a 4-f optical speckle noise simulation module with a parameter generator. In addition, it uses an unpaired dataset for training to overcome the difficulty in obtaining noise-free images and paired data from experiments. The proposed method was tested on both simulated and experimental data, with results showing a 6.9% performance improvement compared with a conventional method and a 2.6% performance improvement compared with unsupervised deep learning in terms of the peak signal-to-noise ratio. Thus, the proposed method exhibits superior denoising performance and potential for DHI, being particularly suitable for processing large datasets.

A New Method for Detecting Weld Stability Based on Color Digital Holography

“…It can automatically extract image features and is widely used in fields such as computer vision, image recognition, speech recognition, and medical image analysis. The research uses the Cycle Generative Adversarial Network (CycleGAN) [29] and 4-f optical simulation integrated cycle-consistent generative adversarial network (4f-CycleGAN) algorithms [30] of deep learning techniques to reduce speckle noise in wrapped phases. Compared to the original Generative Adversarial Network (GAN), CycleGAN is an unsupervised deep learning denoising method that does not require data pairing.…”