Extending the Imaging Depth of Field through Scattering Media by Wavefront Shaping of Non-Diffraction Beams

Author(s):

Han, Tongyu; Peng, Tong; Li, Runze; Wang, Kaige; Sun, Dan & Yao, Baoli

Abstract:

“Increasing the depth of field (DOF) is a crucial issue for imaging through scattering media. In this paper, an improved genetic algorithm is used to modulate the wavefront of light through scattering media, by which high-quality refocusing and imaging through scattering media are achieved. Then, the DOF of the imaging system is effectively extended by further modulating the refocused beam into a non-diffraction beam. Two kinds of non-diffraction beams, i.e., a Bessel beam and Airy beam, were produced as a demonstration. The experimental results show that compared to the Gaussian beam, the DOF of the imaging system by combining the wavefront shaping and non-diffraction Bessel beam or Airy beam can be improved by a factor of 1.1 or 1.5, respectively. The proposed method is helpful for the technical development of high-quality imaging through scattering media with a large DOF.”

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Publication: Photonics
Issue/Year: Photonics, Volume 10; Number 5; Pages 497; 2023
DOI: 10.3390/photonics10050497

Optical classification and reconstruction through multimode fibers

Author(s):

Kürekci, Şahin

Abstract:

“When a light beam travels through a highly scattering medium, two-dimensional random intensity distributions (speckle patterns) are formed due to the complex scattering within the medium. Although they contain valuable information about the input signal and the characteristics of the propagation medium, the speckle patterns are difficult to unscramble, which makes imaging through scattering media an extremely challenging task. Multimode fibers behave similarly to scattering media since they scramble the input information through modal dispersion and create speckle patterns at the distal end. Because multimode fibers are compact and low-cost structures with the ability to transmit large amounts of data simultaneously for long distances, decoding the speckle patterns formed by a multimode fiber and reconstructing the input information has great implications in a wide range of applications, including fiber optic communication, sensor technology, optical imaging, and invasive biomedical applications such as endoscopy. In this thesis, we decode the speckle patterns and reconstruct the input information on the proximal end of a multimode fiber in three different scenarios. Our choice of input signals consists of numbers encoded as binary digits, handwritten letters, and optical frequencies. We train a deep learning model to classify and reconstruct the handwritten letters, while for the rest of the cases, we construct a transmission matrix between the input signals and the output speckle patterns, and solve the inverse propagation equation algebraically. In all cases, the relation between a speckle pattern and the corresponding input signal is learned with low error rates; thus, the signals are classified and reconstructed successfully using the speckle patterns they created. Classifying digits, letters, or images with speckle information aims to build useful systems in optical imaging, communication, and cryptography, while the classification of optical frequencies paves the way for building novel spectrometers. In addition to replicating the currently existing compact, low-budget, and high-resolution multimode fiber spectrometer, we also build a single-pixel fiber spectrometer in order to increase the compactness on the detection side and expand the application areas of the system. The single-pixel spectrometer we offer is based on the integrated intensity measurements of a fixed target region, where the light is focused by shaping the wavefront with a spatial light modulator. Spatial light modulators and wavefront shaping techniques are also utilized in other classification tasks in this thesis to generate the desired input signals.”

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Publication: Middle East Technical University, Thesis
Issue/Year: Graduate School of Natural and Applied Sciences, Thesis, 2022
DOI: https://hdl.handle.net/11511/101287

Generation of controllable spectrum in multiple positions from speckle patterns

Author(s):

Li, Haoran; Wu, Xiaoyan; Liu, Guodong; Vinu, R. V.; Wang, Xiaoyan; Chen, Ziyang & Pu, Jixiong

Abstract:

“Feedback-based wavefront shaping has been proposed to modulate the speckle field generated by coherent light transmitting through scattering media. Different from a monochromatic light, a colorful speckle pattern is generated when polychromatic light transmits through scattering media. Although single-position spectrum modulation has been realized, multiple-position spectrum modulation is a much more complicated problem. Based on non-dominated sorting genetic algorithm II (NSGA2), we design a step-by-step strategy to solve this problem. The results show that modulated spectra in two spatial positions with controllable spectral shape, range and magnitude can be achieved. This research is expected to be applied in the field of adaptive spectral control ranging from advanced spectral filtering to optical fiber dispersion and multi-spectral imaging.”

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Publication: Optics & Laser Technology
Issue/Year: Optics & Laser Technology, Volume 149; Pages 107820; 2022
DOI: 10.1016/j.optlastec.2021.107820

Reconstructing images of two adjacent objects passing through scattering medium via deep learning

Author(s):

Lai, Xuetian; Li, Qiongyao; Chen, Ziyang; Shao, Xiaopeng & Pu, Jixiong

Abstract:

“In this paper, to the best of our knowledge, we first present a deep learning based method for reconstructing the images of two adjacent objects passing through scattering media. We construct an imaging system for imaging of two adjacent objects located at different depths behind the scattering medium. In general, as the light field of two adjacent objects passes through the scattering medium, a speckle pattern is obtained. We employ the designed adversarial network, which is called as YGAN, for reconstructing the two images simultaneously from the speckle. It is shown that based on the trained YGAN, we can reconstruct images of the two adjacent objects with high quality. In addition, the influence of object image types, and the location depths of the two adjacent objects on the imaging fidelity will be studied. Results demonstrate the strong generalization ability and effectiveness of the YGAN. Even in the case where another scattering medium is inserted between the two objects, the YGAN can reconstruct the object images with high fidelity. The technique presented in this paper can be used for applications in areas of medical image analysis, such as medical image classification, segmentation, and studies of multi-object scattering imaging, three-dimensional imaging etc.”

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Publication: Optics Express
Issue/Year: Optics Express, Volume 29; Number 26; Pages 43280; 2021
DOI: 10.1364/oe.446630

Predicting optical transmission through complex scattering media from reflection patterns with deep neural networks

Author(s):

Skarsoulis, Kyriakos; Kakkava, Eirini & Psaltis, Demetri

Abstract:

“Deep neural networks (DNNs) are used to reconstruct transmission speckle intensity patterns from therespective reflection speckle intensity patterns generated by illuminated parafilm layers. The dependence ofthe reconstruction accuracy on the thickness of the sample is examined for different illumination patterns ofvarious feature sizes. High reconstruction accuracy is obtained even for large parafilm thicknesses, for whichthe memory effect of the sample is vanishingly small. The generalization capability of the DNN is also studiedfor unseen scatterers of the same type.”

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Publication: Optics Communications
Issue/Year: Optics Communications, Volume 492; Pages 126968; 2021
DOI: 10.1016/j.optcom.2021.126968

Non-diffracting and self-accelerating Bessel beams with on-demand tailored intensity profiles along arbitrary trajectories

Author(s):

Yan, Wenxiang; Gao, Yuan; Yuan, Zheng; Wang, Zhuang; Ren, Zhi-Cheng; Wang, Xi-Lin; Ding, Jianping & Wang, Hui-Tian

Abstract:

“Owing to their robustness against diffraction, Bessel beams (BBs) offer special advantages in various applications. To enhance their applicability, we present a method to generate self-accelerating zeroth-order BBs along predefined trajectories with tunable z-direction intensity profiles. The character of tunable direction intensity profiles in non-diffracting self-accelerating BBs potentially can attract interest in the regimes of particle manipulation, microfabrication, and free-space optical interconnects.”

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Publication: Optics Letters
Issue/Year: Optics Letters, Volume 46; Number 7; Pages 1494; 2021
DOI: 10.1364/ol.418928

Displacement-agnostic coherent imaging through scatter with an interpretable deep neural network

Author(s):

Li, Yunzhe; Cheng, Shiyi; Xue, Yujia & Tian, Lei

Abstract:

“Coherent imaging through scatter is a challenging task. Both model-based and data-driven approaches have been explored to solve the inverse scattering problem. In our previous work, we have shown that a deep learning approach can make high-quality and highly generalizable predictions through unseen diffusers. Here, we propose a new deep neural network model that is agnostic to a broader class of perturbations including scatterer change, displacements, and system defocus up to 10× depth of field. In addition, we develop a new analysis framework for interpreting the mechanism of our deep learning model and visualizing its generalizability based on an unsupervised dimension reduction technique. We show that our model can unmix the scattering-specific information and extract the object-specific information and achieve generalization under different scattering conditions. Our work paves the way to a robust and interpretable deep learning approach to imaging through scattering media.”

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Publication: Optics Express
Issue/Year: Optics Express, Volume 29; Number 2; Pages 2244; 2021
DOI: 10.1364/oe.411291

Direct comparison of anti-diffracting optical pin beams and abruptly autofocusing beams

Author(s):

Denghui Li, Domenico Bongiovanni, Michael Goutsoulas, Shiqi Xia, Ze Zhang, Yi Hu, Daohong Song, Roberto Morandotti, Nikolaos K. Efremidis, and Zhigang Chen

Abstract:

“We propose and demonstrate a generalized class of anti-diffracting optical pin-like beams (OPBs). Such beams exhibit autofocusing dynamics while morphing into a Bessel-like shape during long-distance propagation, where the size of their main lobe can be tuned by an exponent’s parameter. In particular, their amplitude envelope can be engineered to preserve the pin-like peak intensity pattern. In both theory and experiment, the OPBs are directly compared with radially symmetric abruptly autofocusing beams (AABs) under the same conditions. Furthermore, enhanced transmission and robustness of the OPBs are observed while traversing a scattering colloidal suspension, as compared to both AABs and conventional Bessel beams.”

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Publication: OSA Continuum
Issue/Year: Vol. 3, Issue 6, pp. 1525-1535 (2020)
DOI: 10.1364/OSAC.391878

Retrieving the optical transmission matrix of a multimode fiber using the extended Kalman filter

Author(s):

Guoqiang Huang and Daixuan Wu and Jiawei Luo and Yin Huang and Yuecheng Shen

Abstract:

” Characterizing the transmission matrix (TM) of a multimode fiber (MMF) benefits many fiber-based applications and allows in-depth studies on the physical properties. For example, by modulating the incident field, the knowledge of the TM allows one to synthesize
any optical field at the distill end of the MMF. However, the extraction of optical fields usually requires holographic measurements with interferometry, which complicates the system design and introduces additional noise. In this work, we developed an efficient method to retrieve the TM of the MMF in a referenceless optical system. With pure intensity measurements, this method uses the extended Kalman filter (EKF) to recursively search for the optimum solution. To facilitate the computational process, a modified speckle-correlation scatter matrix (MSSM) is constructed as a low-fidelity initial estimation. This method, termed EKF-MSSM, only requires 4N intensity measurements to precisely solve for N unknown complex variables in the TM. Experimentally, we successfully retrieved the TM of the MMF with high precision, which allows optical focusing with the enhancement (>70%) close to the theoretical value. We anticipate that this method will serve as a useful tool for studying physical properties of the MMFs and potentially open new possibilities in a variety of applications in fiber optics.”

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Publication: Optics Express
Issue/Year: Vol. 28, Issue 7, pp. 9487-9500
DOI: 10.1364/OE.389133

Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media

Author(s):

Yunzhe Li and Yujia Xue and Lei Tian

Abstract:

“Imaging through scattering is an important yet challenging problem. Tremendous progress has been made by exploiting the deterministic input–output “transmission matrix” for a fixed medium. However, this “one-to-one” mapping is highly susceptible to speckle decorrelations – small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical “one-to-all” deep learning (DL) technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable DL approach for imaging through scattering media.”

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Publication: Optica
Issue/Year: Optica Volume 5, Issue 10 pp. 1181-1190 (2018)
DOI: 10.1364/OPTICA.5.001181
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