Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration

Background 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 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 324 paired high/low-SNR images ranging from four to 282 megapixels across 12 sub-datasets that vary in specimen, objective used, staining type, excitation wavelength, and exposure time. The dataset also includes spinning disk confocal microscopy examples and extreme-noise cases. We evaluated three state-of-the-art deep learning denoising models on the dataset: a supervised transformer-based model, a supervised CNN model, and an unsupervised single image model. 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 highest denoising performance but required the longest training time. Competing Interest Statement The authors have declared no competing interest. Footnotes ↵* nazbuhn{at}gmail.com, ghagen{at}uccs.edu, jventu09{at}calpoly.edu Abbreviations - BM3D - block matching and 3D filtering - CARE - content-aware image restoration - CNN - convolutional neural network - GDFN - Gated-Dcov Feed-Forward Network - GPU - graphics processing unit - MDTA - multi-Dconv transposed attention - MP - megapixels - NA - numerical aperture - PSNR - peak signal-to-noise ratio - ROS - reactive oxygen species - SNR - signal to noise ratio - SSIM - structural similarity index measure.