[1] Zhu L, Shao X P. Research progress on scattering imaging technology[J]. Acta Optica Sinica, 40, 0111005(2020).
[2] 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).
[3] Rivenson Y, Zhang Y, Günaydın H et al. Phase recovery and holographic image reconstruction using deep learning in neural networks[J]. Light: Science & Applications, 7, 17141(2018).
[4] Lü M, Wang W, Wang H et al. Deep-learning-based ghost imaging[J]. Scientific Reports, 7, 17865(2017).
[5] Wang H D, Rivenson Y, Jin Y Y et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy[J]. Nature Methods, 16, 103-110(2019).
[6] Li S, Deng M, Lee J et al. Imaging through glass diffusers using densely connected convolutional networks[J]. Optica, 5, 803-813(2018).
[7] Li Y Z, Xue Y J, Tian L. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media[J]. Optica, 5, 1181-1190(2018).
[8] Guo E, Zhu S, Sun Y et al. Learning-based method to reconstruct complex targets through scattering medium beyond the memory effect[J]. Optics Express, 28, 2433-2446(2020).
[9] Zhu S, Guo E L, Cui Q Y et al. Locating and imaging through scattering medium in a large depth[J]. Sensors, 21, 90(2020).
[10] Wang Y, Lin Z S, Wang H et al. High-generalization deep sparse pattern reconstruction: feature extraction of speckles using self-attention armed convolutional neural networks[J]. Optics Express, 29, 35702-35711(2021).
[11] Yann L C, Corinna C, Christopher J B[2021-11-12]. MNIST handwritten digit database [2021-11-12].http:∥yann.lecun.com/exdb/mnist..
[12] Huang G. Liu Z, van der Maaten L, et al. Densely connected convolutional networks[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2261-2269(2017).