Scalable non-invasive imaging through dynamic scattering media at low photon flux

Abstract When the intensity of light reduces to single-photon level, the shot noise becomes dominant. Random scatter, especially the time-varying media will highly increase the photon-limited imaging challenge that the traditional imaging means are inadequate to cope with. In this paper, we develop a new scalable “one to all” imaging approach which focuses on dynamic scattering imaging under photon-limited condition. When the dynamic media optical density is five, we apply this principle to inverse scattering problem and retrieve high-quality images in a real-time way using as little as ∼ 0.4 valid detected photons per pixel on average successfully. The effects of lighting intensities and perturbations are analyzed to highlight special significance of the work. The ultimate results demonstrate that one trained strategy is robust to a wide range of statistical variations in dynamic media, which outperforms common “one to one” method and improves the practical utility efficaciously. This non-invasive imaging work promises a wide prospect in photon-limited scattering applications such as in vivo bioimaging and so on.

[1]  Yibo Zhang,et al.  Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery , 2018, Optica.

[2]  A. Kienle,et al.  Optical properties of fat emulsions. , 2008, Optics express.

[3]  Joseph W. Goodman,et al.  WAVEFRONT‐RECONSTRUCTION IMAGING THROUGH RANDOM MEDIA , 1966 .

[4]  H. Engl,et al.  Tikhonov regularization applied to the inverse problem of option pricing: convergence analysis and rates , 2005 .

[5]  R. R. Alfano,et al.  Methods for Detecting Weak Light Signals , 1968 .

[6]  Felix Heide,et al.  Deep-inverse correlography: towards real-time high-resolution non-line-of-sight imaging , 2020, Optica.

[7]  Kimble,et al.  Precision measurement beyond the shot-noise limit. , 1987, Physical review letters.

[8]  A. Ozcan,et al.  On the use of deep learning for computational imaging , 2019, NanoScience + Engineering.

[9]  Guohai Situ,et al.  Learning-based lensless imaging through optically thick scattering media , 2019, Advanced Photonics.

[10]  Michael Unser,et al.  Convolutional Neural Networks for Inverse Problems in Imaging: A Review , 2017, IEEE Signal Processing Magazine.

[11]  Aggelos K. Katsaggelos,et al.  Variational Bayesian Methods For Multimedia Problems , 2014, IEEE Transactions on Multimedia.

[12]  Filippo Menczer,et al.  Erratum: Competition among memes in a world with limited attention , 2013, Scientific Reports.

[13]  J. Bertolotti,et al.  Non-invasive imaging through opaque scattering layers , 2012, Nature.

[14]  Sungsam Kang,et al.  Deep optical imaging within complex scattering media , 2020, Nature Reviews Physics.

[15]  Yongkeun Park,et al.  Digital optical phase conjugation for delivering two-dimensional images through turbid media , 2013, Scientific Reports.

[16]  Miles J. Padgett,et al.  How many photons does it take to form an image? , 2020, Applied Physics Letters.

[17]  Rebecca Willett,et al.  This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms—Theory and Practice , 2010, IEEE Transactions on Image Processing.

[18]  George Barbastathis,et al.  Imaging through glass diffusers using densely connected convolutional networks , 2017, Optica.

[19]  A. Ishimaru Wave propagation and scattering in random media and rough surfaces , 1991, Proc. IEEE.

[20]  George Barbastathis,et al.  Low Photon Count Phase Retrieval Using Deep Learning. , 2018, Physical review letters.

[21]  Akira Ishimaru,et al.  Wave propagation and scattering in random media , 1997 .

[22]  Rebecca Willett,et al.  Poisson Noise Reduction with Non-local PCA , 2012, Journal of Mathematical Imaging and Vision.

[23]  Hyun-seok Min,et al.  Quantitative Phase Imaging and Artificial Intelligence: A Review , 2018, IEEE Journal of Selected Topics in Quantum Electronics.

[24]  Sylvain Gigan,et al.  Enhanced nonlinear imaging through scattering media using transmission matrix based wavefront shaping , 2016, 1603.07092.

[25]  Tae Joong Eom,et al.  In vivo study of optical speckle decorrelation time across depths in the mouse brain. , 2017, Biomedical optics express.

[26]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[27]  S. Popoff,et al.  Measuring the transmission matrix in optics: an approach to the study and control of light propagation in disordered media. , 2009, Physical review letters.

[28]  Cory M. Simon,et al.  Correction: Corrigendum: Kinetically tuned dimensional augmentation as a versatile synthetic route towards robust metal–organic frameworks , 2015, Nature Communications.

[29]  Lei Tian,et al.  Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media , 2018, Optica.

[30]  Guihua Zeng,et al.  Image reconstruction through dynamic scattering media based on deep learning. , 2019, Optics express.

[31]  Puxiang Lai,et al.  Optical focusing deep inside dynamic scattering media with near-infrared time-reversed ultrasonically encoded (TRUE) light , 2015, Nature Communications.

[32]  Giuliano Scarcelli,et al.  Memory-effect based deconvolution microscopy for super-resolution imaging through scattering media , 2016, Scientific Reports.