Digital Holographic Interferometry (DHI) provides a non-contact measurement at the wavelength level of light. Due to the application of the interferometric principle, non-Gaussian speckle noise introduced during the measurement process is unavoidable and is difficult to eliminate. Thus, denoising is critical and affects measurement accuracy. A method for speckle denoising via self-supervised deep learning, based on a Cycle-Generative Adversarial Network (CycleGAN), is proposed in this paper. The method employs unpaired datasets and integrates a 4-f optical speckle noise simulation module to reduce training costs while improving training accuracy. The proposed method was tested on both simulated and experimental data, with results showing a 4.6% performance improvement in PSNR over competitor algorithms. The proposed method has great potential and advantages in DHI studies with huge datasets.
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