Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image

TL;DR

Noise2SR is a novel self-supervised denoising method for fluorescence microscopy images that efficiently restores image details from a single noisy observation, outperforming existing methods in real scene noise removal.

Contribution

The paper introduces Noise2SR, a new self-supervised denoising approach that trains on paired noisy images of different sizes, improving efficiency and detail restoration from a single noisy image.

Findings

Outperforms existing blind-spot self-supervised denoising methods.

Effective on both simulated and real microscopy noise.

Enhances image detail recovery from minimal data.

Abstract

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.