A neural lens for super-resolution biological imaging. (17th June 2019)

• Record Type: Journal Article
• Authors: Grant-Jacob, James A; Mackay, Benita S; Baker, James A G; Xie, Yunhui; Heath, Daniel J; Loxham, Matthew; Eason, Robert W; Mills, Ben
• Is Part Of: Journal of physics communications. Volume 3:Number 6(2019)
• Publication Date: 2019-06-17
• Subjects: microscopy -- deep learning -- bioimaging -- machine learning; Physics -- Periodicals; 530.05
• DOI: 10.1088/2399-6528/ab267d
• Languages: English
• ISSNs: 2399-6528

Abstract

Abstract: Visualizing structures smaller than the eye can see has been a driving force in scientific research since the invention of the optical microscope. Here, we use a network of neural networks to create a neural lens that has the ability to transform 20× optical microscope images into a resolution comparable to a 1500× scanning electron microscope image. In addition to magnification, the neural lens simultaneously identifies the types of objects present, and hence can label, colour-enhance and remove specific types of objects in the magnified image. The neural lens was used for the imaging of Iva xanthiifolia and Galanthus pollen grains, showing the potential for low cost, non-destructive, high-resolution microscopy with automatic image processing.