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

Guo, Enlai; Zhu, Shuo; Sun, Yan; Bai, Lianfa; Han, Jing

doi:10.1364/oe.383911

Cited by 68 publications

(22 citation statements)

References 31 publications

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“…Other techniques [Chang and Wetzstein 2018;Liao et al 2019] improve robustness by adding sparsity priors on the latent image. Complementary to these techniques are works [Guo et al 2020;Li et al 2018b] that use learning-based approaches to allow recovering illuminator patterns wider than the memory effect range. However, these come at the cost of reduced generality-only patterns similar to those available in constrained training datasets (e.g., handwritten digits) can be recovered.…”

Section: Related Workmentioning

confidence: 99%

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Imaging with Local Speckle Intensity Correlations: Theory and Practice

et al. 2021

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Recent advances in computational imaging have significantly expanded our ability to image through scattering layers such as biological tissues by exploiting the auto-correlation properties of captured speckle intensity patterns. However, most experimental demonstrations of this capability focus on the far-field imaging setting, where obscured light sources are very far from the scattering layer. By contrast, medical imaging applications such as fluorescent imaging operate in the near-field imaging setting, where sources are inside the scattering layer. We provide a theoretical and experimental study of the similarities and differences between the two settings, highlighting the increased challenges posed by the near-field setting. We then draw insights from this analysis to develop a new algorithm for imaging through scattering that is tailored to the near-field setting by taking advantage of unique properties of speckle patterns formed under this setting, such as their local support. We present a theoretical analysis of the advantages of our algorithm and perform real experiments in both far-field and near-field configurations, showing an order-of magnitude expansion in both the range and the density of the obscured patterns that can be recovered.

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Section: Related Workmentioning

confidence: 99%

“…Therefore, we can recover O from Ī ⋆ Ī using phase retrieval algorithms, e.g., the classical algorithm by Fienup [1982] or more robust strategies [Guo et al 2020;Li et al 2018b]. We refer to this procedure as the full-frame auto-correlation algorithm in the rest of the paper.…”

Section: Problem Setting and Backgroundmentioning

confidence: 99%

Imaging with Local Speckle Intensity Correlations: Theory and Practice

et al. 2021

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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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“…The existing technologies to look through an opaque medium mainly include adaptive optics technology [3,4], optical coherence tomography [5,6], and methods based on point spread function or transmission matrix [7][8][9][10][11]. Methods based on speckle correlation and machine learning are also increasingly being used [12][13][14][15][16][17][18]. Physical modeling-based methods have a limitation on optimization and solution capabilities.…”

Section: Introductionmentioning

confidence: 99%

“…Yiwei Sun et al use the Generative Adversarial Network (GAN) to improve the quality of the recovered image through adaptive scattering media [21]. PDSNet proposed by Enlai Guo et al to look through the diffuser and the field of view (FOV) expended up to 40 times the optical memory effect (OME) [17]. In addition, there are some methods that use machine learning to reconstruct face targets with more details.…”

Section: Introductionmentioning

confidence: 99%

Imaging Complex Targets through a Scattering Medium Based on Adaptive Encoding

et al. 2022

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The scattering of light after passing through a complex medium poses challenges in many fields. Any point in the collected speckle will contain information from the entire target plane because of the randomness of scattering. The detailed information of complex targets is submerged in the aliased signal caused by random scattering, and the aliased signal causes the quality of the recovered target to be degraded. In this paper, a new neural network named Adaptive Encoding Scattering Imaging ConvNet (AESINet) is constructed by analyzing the physical prior of speckle image redundancy to recover complex targets hidden behind the opaque medium. AESINet reduces the redundancy of speckle through adaptive encoding which effectively improves the separability of data; the encoded speckle makes it easier for the network to extract features, and helps restore the detailed information of the target. The necessity for adaptive encoding is analyzed, and the ability of this method to reconstruct complex targets is tested. The peak signal-to-noise ratio (PSNR) of the reconstructed target after adaptive encoding can be improved by 1.8 dB. This paper provides an effective reference for neural networks combined with other physical priors in scattering processes.

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

Cited by 68 publications

References 31 publications

Imaging with Local Speckle Intensity Correlations: Theory and Practice

Imaging with Local Speckle Intensity Correlations: Theory and Practice

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

Imaging Complex Targets through a Scattering Medium Based on Adaptive Encoding

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