Comparison of deep learning approaches for extreme low-SNR image restoration

Live-cell fluorescence microscopy enables the study of dynamic cellular processes. However, fluorescence microscopy can damage cells and disrupt these dynamic processes through photobleaching and phototoxicity. Reducing a sample's light exposure mitigates the effects of photobleaching and phototoxicity but results in low signal-to-noise ratio (SNR) images. Deep learning provides a solution for restoring these low-SNR images. However, these deep learning methods require large, representative datasets for training, testing, and benchmarking, as well as substantial GPU memory, particularly for denoising large images.

We present a new fluorescence microscopy dataset designed to expand the range of imaging conditions and specimens currently available for evaluating denoising methods. The dataset contains 17,568 paired high/low-SNR images across 15 sub-datasets that vary in specimen, imaging modality, objective, staining type, excitation wavelength, and exposure time. We evaluated five state-of-the-art deep learning denoising models on the dataset, including supervised, unsupervised, and zero-shot techniques. We also developed an image stitching method that enables large images to be processed in smaller crops and reconstructed.

Our dataset provides a diverse benchmark for evaluating deep learning denoising methods, and our stitching method provides a solution to GPU memory constraints encountered when processing large images. Among the evaluated deep learning models, the supervised Transformer-based model had the best denoising performance but required the longest training time.