Fast Multi-Dimensional Imaging Using the Unsupervised 3D Noise2Void Denoising Network

Label-free multidimensional imaging techniques are widely used in biological imaging. Among them, Raman imaging and phase imaging are two representative methods. However, Raman signals are inherently weak, leading to low signal-to-noise ratios (SNR) in rapidly acquired spectral data. Similarly, the imaging speed of phase imaging is constrained by both shot and sensor noise. Postacquisition data denoising, in the form of both "traditional" and deep-learning-based methods, can improve data quality. However, most deep learning-based denoising approaches typically rely on high-SNR data for supervised training and often process each slice of the three-dimensional data separately, which neglects useful correlations along the third dimension. In this study, we propose a denoising method based on the 3D Noise2Void (3D N2V) network, which incorporates all three dimensions during the denoising operation, and does not require extensive, high SNR training data. This method effectively removes noise from Raman hyperspectral and 3D phase imaging data in an unsupervised manner while preserving spectral (λ), axial (z), and temporal (t) correlations. We validate our method on Raman data of yeast cells and phase tomography and dynamic imaging data of COS7 cells. The denoising performance of 3D N2V is compared with other two existing methods, Block Matching and 3D Filtering (BM3D) and 3D Residual Channel Attention Networks (RCAN). Experimental results demonstrate that the 3D N2V network effectively reduces noise while preserving essential information and biological features, improving the limit of detection (LOD), and outperforming existing denoising methods.