[1] [1] D Berman, TreibitzT， AvidanS BermanD，, T Treibitz, TreibitzT， AvidanS BermanD， and S Avidan. Non-local image dehazing. USA: , 1674-1682(2016).

[2] [2] V Holodovsky, SchechnerY Y， LevinA， et al HolodovskyV，, Y Y Schechner, SchechnerY Y， LevinA， et al HolodovskyV，, A Levin and SchechnerY Y， LevinA， et al HolodovskyV，. In-situ multi-view multi-scattering stochastic tomography. USA: , 1-12(2016).

[3] [3] G Li, YangW， WangH， alet， Image transmission through scattering media using ptychographic iterative engine LiG，, W Yang, YangW， WangH， alet， Image transmission through scattering media using ptychographic iterative engine LiG，, H Wang, YangW， WangH， alet， Image transmission through scattering media using ptychographic iterative engine LiG，, et al and YangW， WangH， alet， Image transmission through scattering media using ptychographic iterative engine LiG，. Applied Sciences. 9, (2019).

[4] [4] A Nick, GraceK， HeckelR， et al NickA，, K Grace, GraceK， HeckelR， et al NickA，, R Heckel and GraceK， HeckelR， et al NickA，. Diffuser Cam： lensless single-exposure 3D imaging. Optica. 5(1), 2334-2536(2018).

[5] [5] M Sheinin, SchechnerY Y SheininM， and Y Y Schechner. The next best underwater view. USA: , 3764-3773(2016).

[6] [6] A P Mosk, P， LagendijkA， LeroseyG， et al MoskA, A Lagendijk, P， LagendijkA， LeroseyG， et al MoskA, G Lerosey and P， LagendijkA， LeroseyG， et al MoskA. Controlling waves in space and time for imaging and focusing in complex media. Nature Photonics. 6(5), 283-292(2012).

[7] [7] D Huang, SwansonE A， LinC P， et al HuangD，, E A Swanson, SwansonE A， LinC P， et al HuangD，, C P Lin and SwansonE A， LinC P， et al HuangD，. Optical coherence tomography. Science. 254(5035), 1178-1181(1991).

[8] [8] I M Vellekoop, M， MoskA P VellekoopI and A P Mosk. Focusing coherent light through opaque strongly scattering media. Optics Letters. 32(16), 2309-2311(2007).

[9] [9] I M VellekoopI M Vellekoop. Feedback-based wavefront shaping. Optics Express. 23(9), 12189-12206(2015).

[10] [10] Z Yaqoob, PsaltisD， FeldM S， et al YaqoobZ，, D Psaltis, PsaltisD， FeldM S， et al YaqoobZ，, M S Feld and PsaltisD， FeldM S， et al YaqoobZ，. Optical phase conjugation for turbidity suppression in biological samples. Nature Photonics. 2(2), 110-115(2008).

[11] [11] K Si, FiolkaR， CuiM SiK，, R Fiolka, FiolkaR， CuiM SiK， and M Cui. Fluorescence imaging beyond the ballistic regime by ultrasound pulse guided digital phase conjugation. Nature Photonics. 6(10), 657-661(2012).

[12] [12] S Popoff, LeroseyG， FinkM， et al PopoffS，, G Lerosey, LeroseyG， FinkM， et al PopoffS，, M Fink and LeroseyG， FinkM， et al PopoffS，. Image transmission through an opaque material. Nature Communications. 1(1), 81-1-81-5(2010).

[14] [14] J Bertolotti, PuttenE G， BlumC， et al BertolottiJ，, E G Putten, PuttenE G， BlumC， et al BertolottiJ，, C Blum and PuttenE G， BlumC， et al BertolottiJ，. Noninvasive imaging through opaque scattering layers. Nature. 491(7423), 232-234(2012).

[15] [15] O Katz, HeidmannP， FinkM， et al KatzO，, P Heidmann, HeidmannP， FinkM， et al KatzO，, M Fink and HeidmannP， FinkM， et al KatzO，. Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations. Nature Photonics. 8(10), 784-790(2014).

[16] [16] G Satat, HeshmatB， NaikN， et al SatatG，, B Heshmat, HeshmatB， NaikN， et al SatatG，, N Naik and HeshmatB， NaikN， et al SatatG，. Advances in ultrafast optics and imaging applications. SPIE Defense Security. 9835, (2016).

[17] [17] Y LeCun, BengioY， HintonG E LeCunY，, Y Bengio, BengioY， HintonG E LeCunY， and G E Hinton. Deep learning. Nature. 521(7553), 436-444(2015).

[18] [18] C Ledig, TheisL， HuszárF， et al LedigC，, L Theis, TheisL， HuszárF， et al LedigC，, F Huszár and TheisL， HuszárF， et al LedigC，. Photo-realistic single image super-resolution using a generative adversarial network. HI. Honolulu: , 105-114(2017).

[19] [19] Y Rivenson, G?r?csZ， GünaydinH， et al RivensonY，, Z G?r?cs, G?r?csZ， GünaydinH， et al RivensonY，, H Günaydin and G?r?csZ， GünaydinH， et al RivensonY，. Deep learning microscopy. Optica. 4(11), 1437-1443(2017).

[20] [20] H C Burger, C， SchulerC J BurgerH and C J Schuler. Image denoising： Can plain neural networks compete with BM3D？. Rhode Island. Providence: , 2392-2399(2012).

[21] [21] K Zhang, ZuoW， ChenY， et al ZhangK，, W Zuo, ZuoW， ChenY， et al ZhangK，, Y Chen and ZuoW， ChenY， et al ZhangK，. Beyond a gaussian denoiser： Residual learning of deep cnn for image denoising. IEEE Trans. Med. Imaging. 26(7), 3142-3155(2017).

[22] [22] O Ronneberger, FischerP， BroxT， et al RonnebergerO，, P Fischer, FischerP， BroxT， et al RonnebergerO，, T Brox and FischerP， BroxT， et al RonnebergerO，. U-Net： Convolutional networks for biomedical image segmentation. Munich: , 234-241(2015).

[23] [23] H Yao, DaiF， ZhangD， et al YaoH，, F Dai, DaiF， ZhangD， et al YaoH，, D Zhang and DaiF， ZhangD， et al YaoH，. Dr2-net： Deep residual reconstruction network for image compressive sensing. Neurocomputing. 359, 483-493(2019).

[24] [24] K Kulkarni, LohitS， TuragaP， et al KulkarniK，, S Lohit, LohitS， TuragaP， et al KulkarniK，, P Turaga and LohitS， TuragaP， et al KulkarniK，. Reconnet： Non-iterative reconstruction of images from compressively sensed measurements. USA: , 449-458(2016).

[25] [25] K H Jin, H， McCannM T， FrousteyE， et al JinK, M T McCann, H， McCannM T， FrousteyE， et al JinK, E Froustey and H， McCannM T， FrousteyE， et al JinK. Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Med. Imaging. 26(9), 4509-4522(2017).

[26] [26] T Nguyen, BuiV， NehmetallahG NguyenT，, V Bui, BuiV， NehmetallahG NguyenT， and G Nehmetallah. Computational optical tomography using 3-D deep convolutional neural networks. Optical Engineering. 57(4), 043111-1-043111-12(2018).

[27] [27] E M Christiansen, M， YangS J， AndoD M， et al ChristiansenE, S J Yang, M， YangS J， AndoD M， et al ChristiansenE, D M Ando and M， YangS J， AndoD M， et al ChristiansenE. In silico labeling： Predicting fluorescent labels in unlabeled images. Cell. 137(3), 792-803(2018).

[28] [28] Y Rivenson, ZhangY， Günayd?nH， et al RivensonY，, Y Zhang, ZhangY， Günayd?nH， et al RivensonY，, H Günayd?n and ZhangY， Günayd?nH， et al RivensonY，. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light-Science & Applications. 7(2), (2018).

[29] [29] Z Ren, XuZ， LamE Y RenZ，, Z Xu, XuZ， LamE Y RenZ， and E Y Lam. Learning-based nonparametric autofocusing for digital holography. Optica. 5(4), 337-344(2018).

[30] [30] T Nguyen, BuiV， LamV， et al NguyenT，, V Bui, BuiV， LamV， et al NguyenT，, V Lam and BuiV， LamV， et al NguyenT，. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection. Optics Express. 25(13), 15043-15057(2017).

[31] [31] A Sinha, LeeJ， LiS， et al SinhaA，, J Lee, LeeJ， LiS， et al SinhaA，, S Li and LeeJ， LiS， et al SinhaA，. Lensless computational imaging through deep learning. Optica. 4(9), 1117-1125(2017).

[32] [32] J S Tyo, S， RoweM P， PughE N， alet， Target detection in optically scattering media by polarization difference imaging TyoJ, M P Rowe, S， RoweM P， PughE N， alet， Target detection in optically scattering media by polarization difference imaging TyoJ, E N Pugh, S， RoweM P， PughE N， alet， Target detection in optically scattering media by polarization difference imaging TyoJ, et al and S， RoweM P， PughE N， alet， Target detection in optically scattering media by polarization difference imaging TyoJ. Appl. Opt. 35(11), 1855-1870(1996).

[33] [33] D Shin, LeeJ J， LeeB G ShinD，, J J Lee, LeeJ J， LeeB G ShinD， and B G Lee. Recognition of a scatteringRecognition 3D object using axially distributed image sensing technique. ARPN J. Eng. Appl. Sci. 9, 2085-2088(2014).

[34] [34] T Ando, HorisakiR， TanidaJ AndoT，, R Horisaki, HorisakiR， TanidaJ AndoT， and J Tanida. Speckle-learning-based object recognition through scattering media. Optics Express. 23(26), 33902-33910(2015).

[35] [35] R Takagi, HorisakiR， TanidaJ TakagiR，, R Horisaki, HorisakiR， TanidaJ TakagiR， and J Tanida. Object recognition through a multi-mode fiber. Optical Review. 24(2), 117-120(2017).

[36] [36] H Chen, GaoY， LiuX， et al ChenH，, Y Gao, GaoY， LiuX， et al ChenH，, X Liu and GaoY， LiuX， et al ChenH，. Imaging through scattering media using speckle pattern classification based support vector regression. Optics Express. 26(20), 26663-26678(2018).

[37] [37] G Satat, TancikM， GuptaO， et al SatatG，, M Tancik, TancikM， GuptaO， et al SatatG，, O Gupta and TancikM， GuptaO， et al SatatG，. Object classification through scattering media with deep learning on time resolved measurement. Optics Express. 25(15), 17466-17479(2017).

[38] [38] B Navid, EiriniK， ChristopheM， et al NavidB，, K Eirini, EiriniK， ChristopheM， et al NavidB，, M Christophe and EiriniK， ChristopheM， et al NavidB，. Learning to see through multimode fibers. Optica. 5(8), 960-966(2018).

[39] [39] P Wang, DiJ WangP， and J Di. Deep learning-based object classification through multimode fiber via a CNN-architecture speckle net. Applied Optics. 57(28), 8258-8263(2018).

[40] [40] A Zdunek, AdamiakA， PieczywekP M， alet， The biospeckle method for the investigation of agricultural crops ZdunekA，, A Adamiak, AdamiakA， PieczywekP M， alet， The biospeckle method for the investigation of agricultural crops ZdunekA，, P M Pieczywek, AdamiakA， PieczywekP M， alet， The biospeckle method for the investigation of agricultural crops ZdunekA，, et al and AdamiakA， PieczywekP M， alet， The biospeckle method for the investigation of agricultural crops ZdunekA，. a review. Optical Lasers Engineering. 52, 276-285(2014).

[41] [41] R Nassif, NaderC A， AfifC， alet， Detection of golden apples’ climacteric peak by laser biospeckle measurements NassifR，, C A Nader, NaderC A， AfifC， alet， Detection of golden apples’ climacteric peak by laser biospeckle measurements NassifR，, C Afif, NaderC A， AfifC， alet， Detection of golden apples’ climacteric peak by laser biospeckle measurements NassifR，, et al and NaderC A， AfifC， alet， Detection of golden apples’ climacteric peak by laser biospeckle measurements NassifR，. Applied Optics. 53(35), 8276-8282(2014).

[42] [42] R Horisaki, TakagiR， TanidaJ HorisakiR，, R Takagi, TakagiR， TanidaJ HorisakiR， and J Tanida. Learning based imaging through scattering media. Optics Express. 24(13), 13738-13743(2016).

[43] [43] H Chen, GaoY， LiuX ChenH，, Y Gao, GaoY， LiuX ChenH， and X Liu. 10711： 107111U-1-107111U-4. Proc. SPIE. (2018).

[44] [44] H Chen, GaoY， LiuX， et al ChenH，, Y Gao, GaoY， LiuX， et al ChenH，, X Liu and GaoY， LiuX， et al ChenH，. Imaging through scattering media via support vector regression. Optics Communications. 126-131(2019).

[45] [45] G E Hinton, E， SalakhutdinovR R HintonG and R R Salakhutdinov. Reducing the dimensionality of data with neural networks. Science. 313(5786), 504-507(2006).

[46] [46] M V Afonso, V， Bioucas-DiasJ M， FigueiredoM A T AfonsoM, J M Bioucas-Dias, V， Bioucas-DiasJ M， FigueiredoM A T AfonsoM and M A T Figueiredo. Fast image recovery using variable splitting and constrained optimization. IEEE Trans. Med. Imaging. 19(9), 2345-2356(2010).

[47] [47] A Lucas, IliadisM， MolinaR， et al LucasA，, M Iliadis, IliadisM， MolinaR， et al LucasA，, R Molina and IliadisM， MolinaR， et al LucasA，. Using deep neural networks for inverse problems in imaging： beyond analytical methods. IEEE Signal Processing Magazine. 35(1), 20-36(2018).