Teams

In recent years, convolutional neural network (CNN)-based methods have shown remarkable performance in the denoising and reconstruction of super-resolved structured illumination microscopy (SR-SIM) data. Therefore, CNN-based architectures have been the main focus of existing studies. Recently, however, an alternative and highly competitive deep learning architecture, Swin Transformer, has been proposed for image restoration tasks. In this work, we present SwinT-fairSIM, a novel method for restoring ...

A new approach to observation and modelling of the Earth System, Environment and Biodiversity. An easy way to visualise, analyse and process environmental data on-demand.

Projects from the FAME team

Fungal Genomics and Industrial Applications Lab

This team is for sharing Galaxy workflows , GA-VirReport and GA-VirReport-Stats which can be used for processing sRNAseq data of host plants for diagnosis of viruses and viroids

Workflows for Galaxy Climate