Imaging consecutive targets through scattering medium and around corners beyond the optical memory effect using untrained network

Shi, Yingjie; Sun, Min; Bai, Lianfa; Mu, Dan; Guo, Enlai

doi:10.1016/j.rinp.2023.106691

Results in Physics

2023

DOI: 10.1016/j.rinp.2023.106691

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Imaging consecutive targets through scattering medium and around corners beyond the optical memory effect using untrained network

Yingjie Shi

Min Sun

Lianfa Bai

et al.

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2024

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Cited by 4 publications

References 40 publications

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Speckle autocorrelation separation method based on frequency intensity for tracking moving object beyond optical memory effect

Shi,

Cui,

Liu

et al. 2024

J. Opt.

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Imaging objects behind a scattering medium beyond the optical memory effect range is vital in various imaging scenarios. Despite numerous proposed imaging methods aimed at separating objects, effectively tracking moving objects remains challenging. In this paper, we present a non-invasive scattering imaging method to reconstruct target beyond the optical memory effect range and track moving objects using a speckle separation and position estimation technique. A speckle autocorrelation separation method based on frequency intensity is proposed to eliminate the noise from the cross-correlation and to separate the objects from the mixed speckles. A position estimation technique is proposed to determine the positional relationships of the objects using speckle deconvolution using the point spread function of imaging regions. A non-invasive imaging system is designed to track the moving object without prior knowledge of the shape and distribution of objects. Experimental validation shows the efficiency of the method for separation and localization, allowing for the reconstruction of the target behind the scattering medium and tracking of the moving object behind the scattering medium. Moreover, the method is capable of separating and tracking a moving object when multiple objects are in the background.

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Speckle autocorrelation separation method based on frequency intensity for tracking moving object beyond optical memory effect

Shi,

Cui,

Liu

et al. 2024

J. Opt.

View full text Add to dashboard Cite

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Single-shot enhanced imaging through multimode fiber based on self-learning networks

Zhang,

Li,

Liang

et al. 2024

Appl. Opt.

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High-fidelity imaging through a multimode fiber (MMF) plays a crucial role in various fields such as medicine, communication, and detection. However, the optical transmission matrix of the MMF suffers from dimensionality reduction. This case results in poor reconstruction quality such as low resolution and noise. In this paper, an unsupervised self-learning circulate learning network is employed to enhance a single degraded image without ground truth images. Moreover, an edge-preserving smoothing filter is applied to address the heavy noise problem of the reconstructed images. Experimental results demonstrate that the proposed method can improve the dimensionality and fidelity of the reconstructed target. Compared to traditional transmission matrix-based reconstruction methods, we have a competitive advantage in terms of evaluation metrics. The proposed method further advances the development of imaging through a multimode fiber.

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Speckle-free self-supervised learning for scalable imaging through scattering media with unseen condition changes

Huang,

Shi,

et al. 2024

Opt. Express

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Recently, deep learning (DL) methods have been extensively developed for imaging through scattering media. However, most learning methods rely on training with pairs of target-speckle data and lack integration with the physical imaging process. Here, we report a speckle-free self-supervised learning method that could be applied for scalable imaging through unknown random diffusers with unseen condition changes. Unlike traditional learning-based methods, our approach optimizes speckle reconstruction by leveraging the physical process of scattering imaging instead of fitting to "speckle-label" pairs. Our method models the scattered light field across potential scattering conditions to generate speckle patterns and extracts their correlation properties for model training and optimization. This eliminates the need for any pre-collected speckle patterns during network training. Our speckle-free method exhibits high reconstruction performance for imaging in unseen scattering conditions. We validate its performance across 440 unseen scattering conditions, including plane displacements, rotations, and combinations. Our method outperforms physics-informed learning approaches regarding reconstruction performance, consistency, and generalization ability in scalable imaging scenarios. This approach addresses the challenges of model generalization and extensive data collection for training, demonstrating its feasibility and superiority for imaging through unknown scattering media in novel scenarios.

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Imaging consecutive targets through scattering medium and around corners beyond the optical memory effect using untrained network

Cited by 4 publications

References 40 publications

Speckle autocorrelation separation method based on frequency intensity for tracking moving object beyond optical memory effect

Speckle autocorrelation separation method based on frequency intensity for tracking moving object beyond optical memory effect

Single-shot enhanced imaging through multimode fiber based on self-learning networks

Speckle-free self-supervised learning for scalable imaging through scattering media with unseen condition changes

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