Content:
Due to the widespread usage of fluorescence microscopy techniques, image analysis methods have become standard tools for modern biologists. Among these methods, image deconvolution is a powerful technique to remove blur and enhance contrast and resolution that can be applied to nearly all fluorescence images.
In this hands-on workshop, we will acquire confocal and STED images of a biological sample and apply different image deconvolution algorithms to the acquired data. We will study how the different algorithms work and implement some of them step by step. On the one hand, this process will give us the opportunity to gain valuable practical experience in image deconvolution and image analysis; on the other hand, we will understand in greater detail how an image is formed in a fluorescence microscope and how noise and artifacts can influence our results. If time allows, we will also cover recent developments in which deep-learning-based context-aware image restoration has been applied to fluorescence microscopy data to remove the noise and enhance spatial resolution.
Learning Outcome:
After the seminar, the students will be familiar with the state-of-art deconvolution methods that can be applied to fluorescence microscopy. Through practical work, each participant will gain hands-on experience in image deconvolution and quantitative image analysis.
Background:
This course assumes prior knowledge about fluorescence microscopy and at least some prior experience in programming. The course works well with the practical course on Bioimaging and/or the practical on Image analysis with Python.
- Trainer/in: Stefano Ceolin
- Trainer/in: Christof Osman