Photonics Research, Volume. 9, Issue 3, B57(2021)
Deep compressed imaging via optimized pattern scanning On the Cover
[1] A. Rogalski. Infrared Detectors(2010).
[6] J. B. Pawley. Handbook of Biological Confocal Microscopy(2006).
[10] E. J. Candès. Compressive sampling. Proceedings of the International Congress of Mathematicians, 1433-1452(2006).
[17] H. Wu, Z. Zheng, Y. Li, W. Dai, H. Xiong. Compressed sensing via a deep convolutional auto-encoder. IEEE Visual Communications and Image Processing (VCIP), 1-4(2018).
[18] J. Zhang, B. Ghanem. ISTA-Net: interpretable optimization-inspired deep network for image compressive sensing. IEEE Conference on Computer Vision and Pattern Recognition, 1828-1837(2018).
[21] L. Fang, F. Monroe, S. W. Novak, L. Kirk, C. R. Schiavon, S. B. Yu, T. Zhang, M. Wu, K. Kastner, Y. Kubota, Z. Zhang, G. Pekkurnaz, J. Mendenhall, K. Harris, J. Howard, U. Manor. Deep learning-based point-scanning super-resolution imaging(2019).
[24] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei. Imagenet: a large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition, 248-255(2009).
[25] K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, A. Ashok. Reconnet: non-iterative reconstruction of images from compressively sensed measurements. IEEE Conference on Computer Vision and Pattern Recognition, 449-458(2016).
[26] D. Martin, C. Fowlkes, D. Tal, J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. 8th IEEE International Conference on Computer Vision (ICCV), 416-423(2001).
[30] O. Ronneberger, P. Fischer, T. Brox. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 234-241(2015).
[32] Y. Chen, Y. Xie, Z. Zhou, F. Shi, A. G. Christodoulou, D. Li. Brain MRI super resolution using 3D deep densely connected neural networks. IEEE 15th International Symposium on Biomedical Imaging (ISBI), 739-742(2018).
[33] G. Huang, Z. Liu, L. Van Der Maaten, K. Q. Weinberger. Densely connected convolutional networks. IEEE Conference on Computer Vision and Pattern Recognition, 4700-4708(2017).
[35] E. D. W. N. Pezzotti, S. Yousefi, M. S. Elmahdy, J. van Gemert, C. Schülke, M. Doneva, T. Nielsen, S. Kastryulin, B. P. F. Lelieveldt, M. J. P. van Osch, M. Staring. Adaptive-CS-Net: FastMRI with adaptive intelligence(2019).
[41] Y. C. Wu, V. Boominathan, H. J. Chen, A. Sankaranarayanan, A. Veeraraghavan. PhaseCam3D-learning phase masks for passive single view depth estimation. IEEE International Conference on Computational Photography(2019).
[44] Y. Li, B. Sixou, F. Peyrin. A review of the deep learning methods for medical images super resolution problems. IRBM(2020).
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Kangning Zhang, Junjie Hu, Weijian Yang. Deep compressed imaging via optimized pattern scanning[J]. Photonics Research, 2021, 9(3): B57
Special Issue: DEEP LEARNING IN PHOTONICS
Received: Oct. 7, 2020
Accepted: Jan. 13, 2021
Published Online: Mar. 2, 2021
The Author Email: Weijian Yang (wejyang@ucdavis.edu)