  Fluorescence microscopy is a key driver to promote discoveries of biomedical
research. However, with the limitation of microscope hardware and
characteristics of the observed samples, the fluorescence microscopy images are
susceptible to noise. Recently, a few self-supervised deep learning (DL)
denoising methods have been proposed. However, the training efficiency and
denoising performance of existing methods are relatively low in real scene
noise removal. To address this issue, this paper proposed self-supervised image
denoising method Noise2SR (N2SR) to train a simple and effective image
denoising model based on single noisy observation. Our Noise2SR denoising model
is designed for training with paired noisy images of different dimensions.
Benefiting from this training strategy, Noise2SR is more efficiently
self-supervised and able to restore more image details from a single noisy
observation. Experimental results of simulated noise and real microscopy noise
removal show that Noise2SR outperforms two blind-spot based self-supervised
deep learning image denoising methods. We envision that Noise2SR has the
potential to improve more other kind of scientific imaging quality.
