Spatiotemporal-Aware Self-Supervised Fluorescence Microscopy Image Denoising

Fluorescence microscopy has been an indispensable tool in many scientific disciplines. However, the expensive imaging cost and the photo-toxicity problem make it difficult to obtain high-quality images. The independent shot noise in fluorescence microscopy images always overwhelms signals and limits the imaging resolution, hindering progress in related research. Recently, self-supervised image denoising has received wide attention for its ability to train a denoiser without paired low Signal-to-Noise Ratio (SNR) and high SNR images. Existing self-supervised fluorescence microscopy image denoising works either suffer from high imaging/computational cost or large training difficulty. Here, we propose a Single-image based Self-Supervised Denoising approach (TriS-D) by utilizing the spatiotemporal redundancy of the fluorescence microscopy imaging data, which facilitates the low-cost and convenient training. The TriS-D can generate the training data from a raw image, not only releasing the demand for multiple low SNR time-lapse imaging data but also enabling the building of a 2D convolution-based model. Comprehensive experiments across different imaging modalities and biological samples verify the effectiveness of the TriS-D.