Optical Technique, Volume. 49, Issue 4, 385(2023)
High-resolution metalens imaging based on SRResNet and point diffusion function design
[1] [1] Li A, Singh S, Sievenpiper D. Metasurfaces and their applications[J]. Nanophotonics,2018,7(6):989-1011.
[2] [2] Xiong B, Xu Y, Wang J, et al. Realizing colorful holographic mimicry by metasurfaces[J].Advanced Materials,2021,33(21):2005864.
[3] [3] Li K, Guo Y, Pu M, et al. Dispersion controlling meta-lens at visible frequency[J]. Optics Express,2017,25(18):21419-21427.
[4] [4] Hua X, Wang Y, Wang S, et al. Ultra-compact snapshot spectral light-field imaging[J]. Nature Communications,2022,13(1):2732.
[5] [5] Gao S, Zhou C, Yue W, et al. Efficient all-dielectric diatomic metasurface for linear polarization generation and 1-bit phase control[J]. ACS Applied Materials & Interfaces,2021,13(12):14497-14506.
[6] [6] Engay E, Huo D, Malureanu R, et al. Polarization-dependent all-dielectric metasurface for single-shot quantitative phase imaging[J]. Nano Letters,2021,21(9):3820-3826.
[7] [7] Li L, Zhang J, Hu Y, et al. Broadband polarization-switchable multi-focal noninterleaved metalenses in the visible[J]. Laser & Photonics Reviews,2021,15(11):2100198.
[8] [8] Kwon H, Arbabi E, Kamali S M, et al. Single-shot quantitative phase gradient microscopy using a system of multifunctional metasurfaces[J]. Nature Photonics,2020,14(2):109-114.
[11] [11] Zhang Y, Xiong B, Zhang Y, et al. DiLFM: an artifact-suppressed and noise-robust light-field microscopy through dictionary learning[J]. Light:Science & Applications,2021,10(1):152.
[12] [12] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]∥Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015:18th International Conference. Munich, Germany:Springer International Publishing,2015:234-241.
[13] [13] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556,2014.
[14] [14] Creswell A, White T, Dumoulin V, et al. Generative adversarial networks: An overview[J]. IEEE Signal Processing Magazine,2018,35(1):53-65.
[16] [16] Tseng E, Colburn S, Whitehead J, et al. Neural nano-optics for high-quality thin lens imaging[J]. Nature Communications,2021,12(1):6493.
[17] [17] Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]∥Proceedings of the IEEE conference on computer vision and pattern recognition.Washington,USA:IEEE,2017:4681-4690.
Get Citation
Copy Citation Text
CHEN Quanliang, YIN Yongyao, JIANG Qiang, HUANG Lingling. High-resolution metalens imaging based on SRResNet and point diffusion function design[J]. Optical Technique, 2023, 49(4): 385