Neuroimage Denoiser for removing noise from transient fluorescent signals in functional imaging

We developed Neuroimage Denoiser, a novel U-Net-based model that effectively removes noise from microscopic recordings of transient local fluorescent signals. The model makes the denoising process independent of the recording frequency and the kinetics of the sensor used. The framework is easy to use for denoising and training and has minimal hardware requirements, thus, making it accessible for an average laboratory to create a custom version specific to their experimental setup. Neuroimage Denoiser significantly enhances the quality of functional microscopy recordings by effectively removing noise, thereby facilitating a more accurate and reliable analysis of neural activity.