Quantitative Phase Imaging Denoising Based on Denoising Diffusion Probabilistic Models

Quantitative Phase Imaging (QPI) has been shown to complement established fluorescence microscopy as well as objective measurements of morphology and dynamics for cellular tissue studies. However, due to its inherent weak-signal measurements, the coherence of the laser light source, the roughness of the object under test or the complex scattering environment, QPI exhibits various types of complex noise, with Poisson-Gaussian noise and scattering noise being the main noise sources. In recent years, significant advancements have been made in the field of deep learning-based denoising algorithms, which have shown considerable efficacy in the denoising of individual noisy data. However, these algorithms have been observed to be less effective when confronted with other types of noisy data, and lack a unified model that can simultaneously remove complex noise from QPI. The present study proposes a QPI denoising approach based on the denoising diffusion probability model (DDPM). The denoising process of DDPM is comprised of two constituent parts: the forward process, which gradually adds standard Gaussian a priori noise to the original image until the image is completely random; and the reverse diffusion chain, which gradually recovers an undisturbed ‘clean’ image by inference from a given a priori noise, thus eliminating various types of complex noise in QPI. A comparative analysis was conducted between the conventional denoising approach based on BM3D and the deep network denoising algorithm with U-Net as the backbone, and the proposed method was evaluated through experimental validation using simulated Gaussian noise, scattering noise and fluorescence microscopy dataset (FMD). The experimental results demonstrate the superior denoising, detail restoration and generalization performance of the proposed method, signifying its significant potential for practical applications.