AUTHOR=Feng Qianghui , Song Qihang , Yan Meng , Huang Zhen Li , Wang Zhengxia TITLE=MSDenoiser: Muti-step adaptive denoising framework for super-resolution image from single molecule localization microscopy JOURNAL=Frontiers in Physics VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2022.1083558 DOI=10.3389/fphy.2022.1083558 ISSN=2296-424X ABSTRACT=

Recent developments in single-molecule localization microscopy (SMLM) enable researchers to study macromolecular structures at the nanometer scale. However, due to the complexity of imaging process, there are a variety of complex heterogeneous noises in SMLM data. The conventional denoising methods in SMLM can only remove a single type of noise. And, most of these denoising algorithms require manual parameter setting, which is difficult and unfriendly for biological researchers. To solve these problems, we propose a multi-step adaptive denoising framework called MSDenoiser, which incorporates multiple noise reduction algorithms and can gradually remove heterogeneous mixed noises in SMLM. In addition, this framework can adaptively learn algorithm parameters based on the localization data without manually intervention. We demonstrate the effectiveness of the proposed denoising framework on both simulated data and experimental data with different types of structures (microtubules, nuclear pore complexes and mitochondria). Experimental results show that the proposed method has better denoising effect and universality.