Acta Optica Sinica, Volume. 43, Issue 5, 0518002(2023)
Large-Field Microscopic Imaging Method Based on Cycle Generative Adversarial Networks
[1] Braunbeck T, Lammer E[M]. Fish embryo toxicity assays(2006).
[2] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C], 1132-1140(2017).
[3] Basak H, Kundu R, Agarwal A et al. Single image super-resolution using residual channel attention network[C], 219-224(2020).
[4] Ledig C, Theis L, Huszár F et al. Photo-realistic single image super-resolution using a generative adversarial network[C], 105-114(2017).
[5] Zhu J Y, Park T, Isola P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C], 2242-2251(2017).
[6] Tang C Y, Pu S L, Ye P Z et al. Fusion of low-illuminance visible and near-infrared images based on convolutional neural networks[J]. Acta Optica Sinica, 40, 1610001(2020).
[7] Zhang W X, Zhu Z C, Zhang Y H et al. Cell image segmentation method based on residual block and attention mechanism[J]. Acta Optica Sinica, 40, 1710001(2020).
[8] Zhu S Q, Wang J, Cai Y F. Low-dose CT denoising algorithm based on improved cycle GAN[J]. Acta Optica Sinica, 40, 2210002(2020).
[9] Li S M, Lei G Q, Fan R. Depth map super-resolution reconstruction based on convolutional neural networks[J]. Acta Optica Sinica, 37, 1210002(2017).
[10] Belthangady C, Royer L A. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction[J]. Nature Methods, 16, 1215-1225(2019).
[11] Wang F, Wang H, Bian Y M et al. Applications of deep learning in computational imaging[J]. Acta Optica Sinica, 40, 0111002(2020).
[12] Zuo C, Feng S J, Zhang X Y et al. Deep learning based computational imaging: status, challenges, and future[J]. Acta Optica Sinica, 40, 0111003(2020).
[13] Qiao C, Li D, Guo Y T et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy[J]. Nature Methods, 18, 194-202(2021).
[14] Chen J J, Sasaki H, Lai H et al. Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes[J]. Nature Methods, 18, 678-687(2021).
[16] Pan Z Q, Yu W J, Yi X K et al. Recent progress on generative adversarial networks (GANs): a survey[J]. IEEE Access, 7, 36322-36333(2019).
[18] Wang X L, Gupta A. Generative image modeling using style and structure adversarial networks[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9908, 318-335(2016).
[19] Yu J H, Lin Z, Yang J M et al. Free-form image inpainting with gated convolution[C], 4470-4479(2019).
[20] Tulyakov S, Liu M Y, Yang X D et al. MoCoGAN: decomposing motion and content for video generation[C], 1526-1535(2018).
[22] Schlegl T, Seeböck P, Waldstein S M et al. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[M]. Niethammer M, Styner M, Aylward S, et al. Information processing in medical imaging. Lecture notes in computer science, 10265, 146-157(2017).
[23] Mertens J F, Zamir S. The value of two-person zero-sum repeated games with lack of information on both sides[J]. International Journal of Game Theory, 1, 39-64(1971).
[24] Ye C, Guan W. A review of application of generative adversarial networks[J]. Journal of Tongji University (Natural Science), 48, 591-601(2020).
[25] Wu H W. Research on single image super-resolution reconstruction algorithm fusing frequency domain information[D], 8-9(2021).
[26] Liu G Q. Research on super resolution image based on deep learning[D], 17-18(2021).
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Rao Fu, Yu Fang, Yong Yang, Dong Xiang, Xiaojing Wu. Large-Field Microscopic Imaging Method Based on Cycle Generative Adversarial Networks[J]. Acta Optica Sinica, 2023, 43(5): 0518002
Category: Microscopy
Received: Aug. 29, 2022
Accepted: Oct. 14, 2022
Published Online: Feb. 27, 2023
The Author Email: Wu Xiaojing (xiaojingwu@nankai.edu.cn)