Learning-based method to reconstruct complex targets through scattering medium beyond the memory effect.

Strong scattering medium brings great difficulties to image objects. Optical memory effect makes it possible to image through strong random scattering medium in a limited angle field-of-view (FOV). The limitation of FOV results in a limited optical memory effect range, which prevents the optical memory effect to be applied to real imaging applications. In this paper, a kind of practical convolutional neural network called PDSNet (Pragmatic De-scatter ConvNet) is constructed to image objects hidden behind different scattering media. The proposed method can expand at least 40 times of the optical memory effect range with a average PSNR above 24dB, and enable to image complex objects in real time, even for objects with untrained scales. The provided experiments can verify its accurateness and efficiency.

[1]  Abbie T Watnik,et al.  Ptychographic imaging of incoherently illuminated extended objects using speckle correlations. , 2019, Applied optics.

[2]  M. Fink,et al.  Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations , 2014, Nature Photonics.

[3]  G. Wetzstein,et al.  Single‐shot speckle correlation fluorescence microscopy in thick scattering tissue with image reconstruction priors , 2018, Journal of biophotonics.

[4]  Guang-Can Guo,et al.  Deep hybrid scattering image learning , 2018, Journal of Physics D: Applied Physics.

[5]  Wei Li,et al.  Imaging through scattering layers exceeding memory effect range by exploiting prior information , 2019, Optics Communications.

[6]  Brandon K. Harvey,et al.  Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue , 2015, Nature Communications.

[7]  Hexiang He,et al.  Exploiting the point spread function for optical imaging through a scattering medium based on deconvolution method , 2019, Journal of Innovative Optical Health Sciences.

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

[9]  Sujit Kumar Sahoo,et al.  Single-shot large field of view imaging with scattering media by spatial demultiplexing. , 2017, Applied optics.

[10]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

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

[12]  Xiangsheng Xie,et al.  Depth detection capability and ultra-large depth of field in imaging through a thin scattering layer , 2019, Journal of Optics.

[13]  Huijuan Li,et al.  Simulation and experimental verification for imaging of gray-scale objects through scattering layers. , 2016, Applied optics.

[14]  Huichang Zhuang,et al.  Imaging of objects through a thin scattering layer using a spectrally and spatially separated reference. , 2018, Optics express.

[15]  Youxin Mao,et al.  High-Quality Tissue Imaging Using a Catheter-Based Swept-Source Optical Coherence Tomography Systems With an Integrated Semiconductor Optical Amplifier , 2011, IEEE Transactions on Instrumentation and Measurement.

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

[17]  J R Fienup,et al.  Phase retrieval algorithms: a comparison. , 1982, Applied optics.

[18]  Florent Krzakala,et al.  Reference-less measurement of the transmission matrix of a highly scattering material using a DMD and phase retrieval techniques. , 2015, Optics express.

[19]  Pingxing Chen,et al.  Imaging through scattering layers exceeding memory effect range with spatial-correlation-achieved point-spread-function. , 2018, Optics letters.

[20]  Wonjun Choi,et al.  Transmission matrix of a scattering medium and its applications in biophotonics. , 2015, Optics express.

[21]  J. Fujimoto,et al.  Optical Coherence Tomography , 1991 .

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

[23]  H. Johnson,et al.  A comparison of 'traditional' and multimedia information systems development practices , 2003, Inf. Softw. Technol..

[24]  A. Friesem,et al.  Real-time wavefront shaping through scattering media by all-optical feedback , 2013, 1303.3161.

[25]  Qionghai Dai,et al.  Prior-information-free single-shot scattering imaging beyond the memory effect. , 2019, Optics letters.

[26]  J. Goodman.  Speckle Phenomena in Optics: Theory and Applications , 2020 .

[27]  Richard G. Baraniuk,et al.  BM3D-PRGAMP: Compressive phase retrieval based on BM3D denoising , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

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

[29]  P. Bahr,et al.  Sampling: Theory and Applications , 2020, Applied and Numerical Harmonic Analysis.

[30]  Sundeep Rangan,et al.  Compressive Phase Retrieval via Generalized Approximate Message Passing , 2014, IEEE Transactions on Signal Processing.

[31]  Navid Borhani,et al.  Learning to see through multimode fibers , 2018, Optica.