Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data.

ABSTRACT:

Background

Convolutional neural network (CNN)-based methods have shown excellent performance in denoising and reconstruction of super-resolved structured illumination microscopy (SR-SIM) data. Therefore, CNN-based architectures have been the focus of existing studies. However, Swin Transformer, an alternative and recently proposed deep learning-based image restoration architecture, has not been fully investigated for denoising SR-SIM images. Furthermore, it has not been fully explored how well transfer learning strategies work for denoising SR-SIM images with different noise characteristics and recorded cell structures for these different types of deep learning-based methods. Currently, the scarcity of publicly available SR-SIM datasets limits the exploration of the performance and

SUBMITTER: Shah ZH 

PROVIDER: S-EPMC10787368 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature