Unsupervised Adaptive Deep Learning Framework for Video Denoising in Light Scattering Imaging

Light scattering is a powerful tool that has been widely applied in various scenarios, such as nanoparticle analysis, single-cell measurement, and blood flow monitoring. However, noise is always a concerning and challenging issue in light scattering imaging (LSI) due to the complexity of noise sources. In this work, a deep learning-based adaptive denoising framework has been established to explore the temporal information on LSI videos, aiming to provide an unsupervised and self-learning denoising strategy for various application scenarios of LSI. This novel framework consists of three stages: noise distribution maps for describing the characteristics of LSI noise, video denoising based on the unsupervised learning of the FastDVDNet network, and denoising effect discrimination to screen the best denoised result for further processing. The denoising performance is validated by two common LSI applications: nanoparticle analysis and label-free identification of single cells. The result shows that our method compares favorably to existing methods in suppressing the background noise and enhancing the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of LSI. Consequently, the successful analysis of both particle size distribution and cell classification can be notably improved. The proposed unsupervised adaptive denoising method is expected to offer a powerful tool toward a fully automated denoising and improved accuracy in extensive applications of LSI.