Improving Blind Spot Denoising for Microscopy

TL;DR

This paper introduces a novel self-supervised denoising method for microscopy images that incorporates the diffraction-limited nature of such images, effectively reducing artifacts and achieving results comparable to supervised approaches.

Contribution

It proposes a new approach that integrates the point spread function into self-supervised denoising, improving image quality without requiring paired training data.

Findings

Reduces high-frequency artifacts in microscopy denoising

Achieves near-supervised quality with self-supervised method

Leverages diffraction-limited assumption for improved results

Abstract

Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem is often addressed via (supervised) deep learning based denoising. Recently, by making assumptions about the noise statistics, self-supervised methods have emerged. Such methods are trained directly on the images that are to be denoised and do not require additional paired training data. While achieving remarkable results, self-supervised methods can produce high-frequency artifacts and achieve inferior results compared to supervised approaches. Here we present a novel way to improve the quality of self-supervised denoising. Considering that light microscopy images are usually diffraction-limited, we propose to include this knowledge in the denoising process. We assume the clean image to be the result of a convolution with a point spread function (PSF) and explicitly include this operation at the end of our neural network. As a consequence, we are able to eliminate high-frequency artifacts and achieve self-supervised results that are very close to the ones achieved with traditional supervised methods.