Coherent noise suppression in digital holographic microscopy based on label-free deep learning

Deep learning techniques can be introduced into the digital holography to suppress the coherent noise. It is often necessary to first make a dataset of noisy and noise-free phase images to train the network. However, noise-free images are often difficult to obtain in practical holographic applications. Here we propose a label-free training algorithms based on self-supervised learning. A dilated blind spot network is built to learn from the real noisy phase images and a noise level function network to estimate a noise level function. Then they are trained together via maximizing the constrained negative log-likelihood and Bayes' rule to generate a denoising phase image. The experimental results demonstrate that our method outperforms standard smoothing algorithms in accurately reconstructing the true phase image in digital holographic microscopy.

Automated summary

This study proposes a new method to suppress coherent noise in digital holography using deep learning techniques. The method does not require noise-free images and instead trains the model to generate denoised phase images through self-supervised learning. Experimental results show that this method outperforms traditional smoothing algorithms in digital holographic microscopy.