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Researcher profile

Shuo Zhu

Shuo Zhu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.IVComputer Visionphysics.optics

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Published work

3 published item(s)

preprint2026arXiv

Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis

This work addresses the critical problem of tracking fast-moving objects through strongly scattering media in a low-light environment. Different from existing approaches that use frame-based cameras with fixed exposure times, which trade off signal-to-noise ratio for temporal resolution, we introduce computational neuromorphic tracking (CNT), a physics-informed framework that combines asynchronous event sensing with task-driven speckle analysis for robust motion estimation. We formulate the neuromorphic speckle aggregation as a spatiotemporal speckle representation, jointly optimizing the temporal and spatial parameters to maximize tracking stability under extreme conditions. Extensive experiments demonstrate that our method enables robust motion tracking of 10x faster motion and under 10x dimmer illumination compared to conventional systems. These improvements significantly broaden the operational regime for tracking through scattering media, providing an efficient and scalable solution for demanding scenarios involving rapid motion and low-light conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Synchronous locating and imaging behind scattering medium in a large depth based on deep learning

Scattering medium brings great difficulties to locate and image planar objects especially when the object has a large depth. In this letter, a novel learning-based method is presented to locate and image the object hidden behind a thin scattering diffuser. A multi-task network, named DINet, is constructed to predict the depth and the image of the hidden object from the captured speckle patterns. The provided experiments verify that the proposed method enables to locate the object with a depth mean error less than 0.05 mm, and image the object with an average PSNR above 24 dB, in a large depth ranging from 350 mm to 1150 mm. The constructed DINet can obtain multiple physical information via a single speckle pattern, including both the depth and image. Comparing with the traditional methods, it paves the way to the practical applications requiring large imaging depth of field behind scattering media.

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preprint2019arXiv

Learning-based real-time method to looking through scattering medium beyond the memory effect

Strong scattering medium brings great difficulties to optical imaging, which is also a problem in medical imaging and many other fields. Optical memory effect makes it possible to image through strong random scattering medium. However, this method also has the limitation of limited angle field-of-view (FOV), which prevents it from being applied in practice. In this paper, a kind of practical convolutional neural network called PDSNet is proposed, which effectively breaks through the limitation of optical memory effect on FOV. Experiments is conducted to prove that the scattered pattern can be reconstructed accurately in real-time by PDSNet, and it is widely applicable to retrieve complex objects of random scales and different scattering media.

0 citations0 reviewsNo code linkedOpen access