Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey
It provides a comprehensive overview for researchers in microscopy and deep learning, but is incremental as it synthesizes existing work rather than introducing novel findings.
This survey paper reviews the problem of enhancing microscopy images for biological and materials analysis, summarizing the evolution, applications, and challenges of deep learning methods in super-resolution, reconstruction, and denoising without presenting new results or numbers.
Microscopy image enhancement plays a pivotal role in understanding the details of biological cells and materials at microscopic scales. In recent years, there has been a significant rise in the advancement of microscopy image enhancement, specifically with the help of deep learning methods. This survey paper aims to provide a snapshot of this rapidly growing state-of-the-art method, focusing on its evolution, applications, challenges, and future directions. The core discussions take place around the key domains of microscopy image enhancement of super-resolution, reconstruction, and denoising, with each domain explored in terms of its current trends and their practical utility of deep learning.