Constructing Large Transmission Matrix of Scattering Sample Based on Super-Resolution Algorithm

A method to construct a large transmission matrix (TM) of highly scattering media based on an algorithm is presented. For imaging through scattering media, measuring a large TM enables image reconstruction with high spatial resolution; however, it is difficult to operate in practice because the time required for the calibration of the TM is proportional to the number of calibrated input modes. Instead of directly achieving calibration using advanced hardware, a large TM is defined and constructed based on a multiframe image super-resolution algorithm, which traditionally aims to recover a high-resolution (HR) image of the original object from several low-resolution images. With sufficient size improvement, the defined large TM is constructed from several naturally measured complementary small TMs, each of them is measured using a subpixel-shifted projector phase mask. The realized large TM, which is constructed through subpixel registration and interpolation operations at the help of a hypothetical HR mask, enables HR image reconstruction from the original object’s speckle signal. The feasibility of the proposed method is proven via optical experiments involving statistical analysis and image reconstruction. The proposed method benefits existing TM-based image reconstruction applications and offers a new perspective on the size of TM measurements and the imaging resolution.

[1]  Michael Elad,et al.  Fast and Robust Multi-Frame Super-Resolution , 2004, IEEE Transactions on Image Processing.

[2]  Dajiang Lu,et al.  Optical Cryptanalysis Method Using Wavefront Shaping , 2017, IEEE Photonics Journal.

[3]  Jie Li,et al.  Image super-resolution: The techniques, applications, and future , 2016, Signal Process..

[4]  Raj Rao Nadakuditi,et al.  Controlling Light Transmission Through Highly Scattering Media Using Semi-Definite Programming as a Phase Retrieval Computation Method , 2016, Scientific Reports.

[5]  Xiubao Sui,et al.  Transmission Matrix Based Image Super-Resolution Reconstruction Through Scattering Media , 2020, IEEE Photonics Journal.

[6]  S. Popoff,et al.  Controlling light through optical disordered media: transmission matrix approach , 2011, 1107.5285.

[7]  Abbie T. Watnik,et al.  Imaging around corners in the mid-infrared using speckle correlations. , 2020, Optics express.

[8]  Peng Jin,et al.  Multi-frame image super-resolution reconstruction via low-rank fusion combined with sparse coding , 2018, Multimedia Tools and Applications.

[9]  C. Beenakker Random-matrix theory of quantum transport , 1996, cond-mat/9612179.

[10]  Jiying Zhao,et al.  Robust Multi-Frame Super-Resolution Based on Spatially Weighted Half-Quadratic Estimation and Adaptive BTV Regularization , 2018, IEEE Transactions on Image Processing.

[11]  Daniel Gross,et al.  Improved resolution from subpixel shifted pictures , 1992, CVGIP Graph. Model. Image Process..

[12]  Rafael Piestun,et al.  Single multimode fiber endoscope. , 2017, Optics express.

[13]  D. Conkey,et al.  High-speed scattering medium characterization with application to focusing light through turbid media. , 2012, Optics express.

[14]  Zelong Wang,et al.  Superresolution imaging by dynamic single-pixel compressive sensing system , 2013 .

[15]  High-speed photoacoustic-guided wavefront shaping for focusing light in scattering media , 2020, Optics letters.

[16]  Steven C. H. Hoi,et al.  Deep Learning for Image Super-Resolution: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Moonseok Kim,et al.  Scanner-free and wide-field endoscopic imaging by using a single multimode optical fiber. , 2012, Physical review letters.

[18]  Tomáš Čižmár,et al.  High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging , 2018, Light: Science & Applications.

[19]  Demetri Psaltis,et al.  Multimode optical fiber transmission with a deep learning network , 2018, Light: Science & Applications.

[20]  Russell C. Hardie,et al.  A Fast Image Super-Resolution Algorithm Using an Adaptive Wiener Filter , 2007, IEEE Transactions on Image Processing.

[21]  Michael Elad,et al.  Multi-Scale Patch-Based Image Restoration , 2016, IEEE Transactions on Image Processing.

[22]  Yan Liu,et al.  Focusing light through scattering media by transmission matrix inversion. , 2017, Optics express.

[23]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Xuelong Li,et al.  A Unified Learning Framework for Single Image Super-Resolution , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[25]  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.

[26]  M. Fink,et al.  Controlling light in scattering media non-invasively using the photoacoustic transmission matrix , 2013, 1305.6246.

[27]  Wei Sheng,et al.  Non-invasive tracking of moving objects behind scattering layers from a single multiplexed speckle , 2021 .

[28]  Marc Reinig,et al.  High-speed scanning interferometric focusing by fast measurement of binary transmission matrix for channel demixing. , 2015, Optics express.

[29]  A. Mosk,et al.  Exploiting disorder for perfect focusing , 2009, 0910.0873.

[30]  Jonghee Yoon,et al.  Measuring optical transmission matrices by wavefront shaping. , 2015, Optics express.

[31]  Hui Chen,et al.  Binary amplitude-only image reconstruction through a MMF based on an AE-SNN combined deep learning model. , 2020, Optics express.

[32]  Sylvain Gigan,et al.  Image transmission through an opaque material. , 2010, Nature communications.

[33]  Caiming Zhang,et al.  Single-Image Super-Resolution Based on Rational Fractal Interpolation , 2018, IEEE Transactions on Image Processing.

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

[35]  O. Katz,et al.  Looking around corners and through thin turbid layers in real time with scattered incoherent light , 2012, Nature Photonics.

[36]  C. Kaminski,et al.  Fast volumetric fluorescence imaging with multimode fibers. , 2020, Optics letters.

[37]  Zhenbing Liu,et al.  MADNet: A Fast and Lightweight Network for Single-Image Super Resolution , 2020, IEEE Transactions on Cybernetics.

[38]  Michael S Feld,et al.  Overcoming the diffraction limit using multiple light scattering in a highly disordered medium. , 2011, Physical review letters.

[39]  S. Gigan,et al.  Focusing light through dynamical samples using fast continuous wavefront optimization. , 2017, Optics letters.

[40]  Chengbo Li An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing , 2010 .

[41]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[42]  J. Bertolotti,et al.  Speckle correlation resolution enhancement of wide-field fluorescence imaging , 2015 .

[43]  YongKeun Park,et al.  Measuring large optical transmission matrices of disordered media. , 2013, Physical review letters.

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

[45]  A. Mosk,et al.  Focusing coherent light through opaque strongly scattering media. , 2007, Optics letters.

[46]  Chia-Hung Yeh,et al.  Learning-Based Joint Super-Resolution and Deblocking for a Highly Compressed Image , 2015, IEEE Transactions on Multimedia.

[47]  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.

[48]  Yefeng Guan,et al.  Memory Effect Based Filter to Improve Imaging Quality Through Scattering Layers , 2018, IEEE Photonics Journal.