Lensless imaging of pollen grains at three-wavelengths using deep learning. (28th July 2020)

• Record Type: Journal Article
• Authors: Grant-Jacob, James A; Praeger, Matthew; Loxham, Matthew; Eason, Robert W; Mills, Ben
• Is Part Of: Environmental research communications. Volume 2:Number 7(2020)
• Publication Date: 2020-07-28
• Subjects: deep learning -- sensing -- optics -- pollen -- particle pollution -- lensless imaging; Environmental sciences -- Periodicals; 333.705
• DOI: 10.1088/2515-7620/aba6d1
• Languages: English
• ISSNs: 2515-7620

Abstract

Abstract: Image reconstruction of pollen grains was performed using neural networks, from light scattering patterns recorded with simultaneous irradiation at three laser wavelengths. The shapes of the reconstructed optical images using one network were shown to have a pixel accuracy on average of 98.9%. Two other neural networks were shown to be able to convert scattering patterns into predictions of z-stack maximum intensity projection microscope images and scanning electron microscopy images. The capability of producing magnified images in a variety of formats directly from scattering patterns will be applicable to particle sensing in a range of fields, including health and safety, environmental protection, ocean and space science.