Study on Aberration Correction of Adaptive Optics Based on Convolutional Neural Network
Jin Li,
Luwei Wang,
Yong Guo,
Yangrui Huang,
Zhigang Yang,
Wei Yan,
Junle Qu
Affiliations
Jin Li
Key Laboratory of Optoelectronic Devices and Systems, Center for Biomedical Optics and Photonics (CBOP), College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Luwei Wang
Key Laboratory of Optoelectronic Devices and Systems, Center for Biomedical Optics and Photonics (CBOP), College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Yong Guo
Key Laboratory of Optoelectronic Devices and Systems, Center for Biomedical Optics and Photonics (CBOP), College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Yangrui Huang
Key Laboratory of Optoelectronic Devices and Systems, Center for Biomedical Optics and Photonics (CBOP), College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Zhigang Yang
Key Laboratory of Optoelectronic Devices and Systems, Center for Biomedical Optics and Photonics (CBOP), College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Wei Yan
Key Laboratory of Optoelectronic Devices and Systems, Center for Biomedical Optics and Photonics (CBOP), College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Junle Qu
Key Laboratory of Optoelectronic Devices and Systems, Center for Biomedical Optics and Photonics (CBOP), College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
The existence of aberrations has always been an important limiting factor in the imaging field. Especially in optical microscopy imaging, the accumulated aberration of the optical system and the biological samples distorts the wavefront on the focal plane, thereby reducing the imaging resolution. Here, we propose an adaptive optical aberration correction method based on convolutional neural network. By establishing the relationship between the Zernike polynomial and the distorted wavefront, with the help of the fast calculation advantage of an artificial intelligence neural network, the distorted wavefront information can be output in a short time for the reconstruction of the wavefront to achieve the purpose of improving imaging resolution. Experimental results show that this method can effectively compensate the aberrations introduced by the system, agarose and HeLa cells. After correcting, the point spread function restored the doughnut-shape, and the resolution of the HeLa cell image increased about 20%.
