Towards Data-driven Fluorescence Microscopy Image Restoration: A Survey

PhD Qualifying Examination


Title: "Towards Data-driven Fluorescence Microscopy Image Restoration: A Survey"

by

Mr. Wenqiang LI


Abstract:

Fluorescence microscopy is essential in both biological and medical research, 
offering crucial insights into the structural and functional dynamics of cells, 
tissues, and complex biological processes. However, issues such as noise, 
photobleaching, limited resolution, and low imaging speed often hinder image 
quality and interpretability. Traditional restoration techniques have only 
partially addressed these issues. Recent advancements in data-driven methods, 
particularly deep learning, present promising solutions that not only restore 
but also enhance fluorescence microscopy images beyond conventional limits. 
This survey reviews state-of-the-art data-driven fluorescence microscopy image 
restoration, covering fundamental principles, challenges, and advancements in 
deep learning techniques. We categorize various approaches, highlight key 
datasets and benchmarking protocols, and examine significant applications in 
biology and medicine, including pathology and oncology. These techniques have 
led to breakthroughs by revealing previously undetectable details and improving 
time resolution. Despite progress, challenges such as the need for diverse 
datasets, computational costs, and model generalization persist. We discuss 
emerging trends and future research directions, including real-time image 
restoration, foundation models, and explainable models. In conclusion, 
data-driven methods have the potential to transform fluorescence microscopy by 
pushing the physical limits of this technology. These advancements can 
significantly enhance image quality and interpretability, thereby expanding the 
boundaries of scientific discovery and life science exploration.


Date:                   Friday, 16 August 2024

Time:                   4:00pm - 6:00pm

Zoom Meeting ID:        451 423 0129

Committee Members:      Dr. Shuai Wang (Supervisor)
                        Dr. Hao Chen (Chairperson)
                        Dr. Dan Xu
                        Dr. Terence Wong (CBE)