MSDenoiser: Muti-step adaptive denoising framework for super-resolution image from single molecule localization microscopy - Feng, Qianghui; Song, Qihang; Yan, Meng; Huang, Zhen Li; Wang, Zhengxia (Frontiers in Physics, 2022)
Recent development 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 for SMLM data can only remove a single type of noise, and most algorithms require manual parameter setting, which is difficult and unfriendly for biological researchers. To solve this problem, we propose a multi-step adaptive denoising framework called MSDenoiser, which incorporates multiple noise reduction algorithms and can gradually remove heterogeneous mixed noise in SMLM localization data. In addition, this framework can adaptively learn algorithm parameters based on the localization data without manually intervention. We demonstrate the effectiveness of proposed denoising framework on simulated data and experimental data with different structures (microtubules, nuclear pore complexes and mitochondria). Experimental results show that the proposed method has better denoising effect and universality.