Imaging through unknown scattering media based on physics-informed learning

Zhu, Shuo; Guo, Enlai; Gu, Jie; Bai, Lianfa; Han, Jing

doi:10.1364/prj.416551

Cited by 102 publications

(19 citation statements)

References 59 publications

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“…However, beyond the memory effect, it is theoretically impossible to build and train neural networks based on speckle autocorrelations to decrypt complex‐structured face images from an unknown scattering medium, due to weak relations between speckle autocorrelations and image autocorrelations. [ 46,47 ] In this work, the memory effect range is less than a quarter of the face image size, thus the cryptosystem is safe under ciphertext‐only attacks. Furthermore, chosen‐plaintext and known‐plaintext attacks are possible only when attackers can get access to at least 10 000 image‐speckle sets, as discussed in Figure S2, Supporting Information.…”

Section: Discussionmentioning

confidence: 99%

Speckle‐Based Optical Cryptosystem and its Application for Human Face Recognition via Deep Learning

Zhao

et al. 2022

Advanced Science

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Face recognition has become ubiquitous for authentication or security purposes. Meanwhile, there are increasing concerns about the privacy of face images, which are sensitive biometric data and should be protected. Software-based cryptosystems are widely adopted to encrypt face images, but the security level is limited by insufficient digital secret key length or computing power. Hardware-based optical cryptosystems can generate enormously longer secret keys and enable encryption at light speed, but most reported optical methods, such as double random phase encryption, are less compatible with other systems due to system complexity. In this study, a plain yet highly efficient speckle-based optical cryptosystem is proposed and implemented. A scattering ground glass is exploited to generate physical secret keys of 17.2 gigabit length and encrypt face images via seemingly random optical speckles at light speed. Face images can then be decrypted from random speckles by a well-trained decryption neural network, such that face recognition can be realized with up to 98% accuracy. Furthermore, attack analyses are carried out to show the cryptosystem's security. Due to its high security, fast speed, and low cost, the speckle-based optical cryptosystem is suitable for practical applications and can inspire other high-security cryptosystems.

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Section: Discussionmentioning

confidence: 99%

Speckle‐Based Optical Cryptosystem and its Application for Human Face Recognition via Deep Learning

Zhao

et al. 2022

Advanced Science

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“…introduced a physics-informed learning method for imaging through unknown diffusers by combining physics-based theories with CNNs. 26 Zheng et al. proposed an end-to-end deep neural network to detect and identify unique features in incoherent images.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal absorption fluctuation imaging based on U-Net

et al. 2022

J. Biomed. Opt.

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. Significance: Full-field optical angiography is critical for vascular disease research and clinical diagnosis. Existing methods struggle to improve the temporal and spatial resolutions simultaneously. Aim: Spatiotemporal absorption fluctuation imaging (ST-AFI) is proposed to achieve dynamic blood flow imaging with high spatial and temporal resolutions. Approach: ST-AFI is a dynamic optical angiography based on a low-coherence imaging system and U-Net. The system was used to acquire a series of dynamic red blood cell (RBC) signals and static background tissue signals, and U-Net is used to predict optical absorption properties and spatiotemporal fluctuation information. U-Net was generally used in two-dimensional blood flow segmentation as an image processing algorithm for biomedical imaging. In the proposed approach, the network simultaneously analyzes the spatial absorption coefficient differences and the temporal dynamic absorption fluctuation. Results: The spatial resolution of ST-AFI is up to , and the temporal resolution is up to 0.032 s. In vivo experiments on 2.5-day-old chicken embryos were conducted. The results demonstrate that intermittent RBCs flow in capillaries can be resolved, and the blood vessels without blood flow can be suppressed. Conclusions: Using ST-AFI to achieve convolutional neural network (CNN)-based dynamic angiography is a novel approach that may be useful for several clinical applications. Owing to their strong feature extraction ability, CNNs exhibit the potential to be expanded to other blood flow imaging methods for the prediction of the spatiotemporal optical properties with improved temporal and spatial resolutions.

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“…Optical systems can be classified into those encoding objects as transmission mode 8 , 14 , 15 , 17 – 19 and reflection mode. 9 – 11 , 13 , 16 , 20 – 24 In both modes, optical systems, such as lensless imaging systems, 8 , 10 , 17 , 20 , 21 , 23 imaging systems with one or two lenses, 9 , 11 , 14 – 16 , 18 , 19 , 22 and imaging systems with a camera lens 13 , 24 have been investigated. Among these, the

optical system consisting of two lenses has the advantage of removing scattered light by spatial filtering, a large field of view, ease of magnification by replacing the lens system, and light-collection efficiency.…”

Section: Introductionmentioning

confidence: 99%

Investigation of image plane for image reconstruction of objects through diffusers via deep learning

Tsukada

Watanabe

2022

J. Biomed. Opt.

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. Significance: The imaging of objects hidden in light-scattering media is a vital practical task in a wide range of applications, including biological imaging. Deep-learning-based methods have been used to reconstruct images behind scattering media under complex scattering conditions, but improvements in the quality of the reconstructed images are required. Aim: To investigate the effect of image plane on the accuracy of reconstructed images. Approach: Light reflected from an object passing through glass diffusers is captured by changing the image plane of an optical imaging system. Images are reconstructed by deep learning, and evaluated in terms of structural similarity index measure, classification accuracy of digital images, and training and testing error curves. Results: The reconstruction accuracy was improved for the case in which the diffuser was imaged, compared to the case where the object was imaged. The training and testing error curves show that the loss converged to lower values in fewer epochs when the diffuser was imaged. Conclusions: The proposed approach demonstrates an improvement in the accuracy of the reconstruction of objects hidden through glass diffusers by imaging glass diffuser surfaces, and can be applied to objects at unknown locations in a scattering medium.

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Imaging through unknown scattering media based on physics-informed learning

Cited by 102 publications

References 59 publications

Speckle‐Based Optical Cryptosystem and its Application for Human Face Recognition via Deep Learning

Speckle‐Based Optical Cryptosystem and its Application for Human Face Recognition via Deep Learning

Spatiotemporal absorption fluctuation imaging based on U-Net

Investigation of image plane for image reconstruction of objects through diffusers via deep learning

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