Denoising for high-magnification bioluminescence imaging of cells by machine learning with physics-based noise modeling of EMCCD

Abstract

Bioluminescence imaging (BLI) detects light emission from samples expressing luciferase in the presence of luciferin. Unlike fluorescence imaging, BLI does not require excitation light, thereby avoiding phototoxicity in living cells and photobleaching of labels. However, the intrinsically slow turnover of the luciferin-luciferase reaction often results in low luminescence intensity, which degrades image quality and limits practical applications, particularly in high-magnification optical microscopy of cells. In this study, we demonstrate that image denoising provides an effective strategy to overcome this fundamental limitation of BLI. We developed a physics-based noise model that incorporates fixed-pattern noise, blooming noise, readout noise, quantization noise, and Poisson shot noise, enabling accurate estimation of noise parameters encountered in EMCCD-based microscopy. In the denoising pipeline, raw noisy images were first corrected for the fixed-pattern and blooming noises, followed by restoration using Uformer neural network trained with paired ground-truth images and synthetically generated noisy images. Quantitative evaluation using the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) showed that denoised images derived from raw data acquired with exposure times of ≤100 ms achieved PSNR and SSIM values comparable to those of raw images acquired with exposure times of 1–3 s. These results indicate that the proposed denoising approach substantially extends the practical limit of high-magnification BLI.

Caption of Graphical Abstract

Bioluminescence images of organelles at high magnification such as mitochondria labeled with luciferase are so dim that they can be clearly visualized only at considerably extended exposure time of, e.g., longer than 1 s. The present paper reports an image denoising technique involving the correction of blooming and fixed pattern noise (FPN) followed by noise reduction using a convolutional neural network (CNN) of Uformer. The present technique is able to restore a clean image with as high quality as that of a corresponding raw image acquired at a 30-fold longer exposure time.