Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network

Abstract

To date, live-cell imaging at the nanometer scale remains challenging. Even though super-resolution microscopy methods have enabled visualization of sub-cellular structures below the optical resolution limit, the spatial resolution is still far from enough for the structural reconstruction of biomolecules in vivo (i.e., ~24 nm thickness of microtubule fiber). In this study, a deep learning network named A-net was developed and shows that the resolution of cytoskeleton images captured by a confocal microscope can be significantly improved by combining the A-net deep learning network with the DWDC algorithm based on a degradation model. Utilizing the DWDC algorithm to construct new datasets and taking advantage of A-net neural network’s features (i.e., considerably fewer layers and relatively small dataset), the noise and flocculent structures which originally interfere with the cellular structure in the raw image are significantly removed, with the spatial resolution improved by a factor of 10. The investigation shows a universal approach for exacting structural details of biomolecules, cells and organs from low-resolution images.

Editorial for the Special Issue on Optics and Photonics in Micromachines

“…In this Special Issue, we are glad to collect 15 research articles covering a broad area, including optical field modulation [ 11 ], laser fabrication techniques [ 12 , 13 ], optical measurement [ 14 , 15 , 16 , 17 , 18 ], on-chip photonic devices [ 19 , 20 , 21 , 22 ], super-resolution imaging [ 23 , 24 ], and related theoretical [ 25 ] investigations.…”

“…There are another two research articles related directly to super-resolution imaging. One used the traditional method [ 23 ] by combining Lucy–Richardson deconvolution and discrete wavelet methods, and the other one used an A-net deep learning network [ 24 ]. Both show apparent improvement in the spatial resolution and SNR of biological images.…”