arXiv subjects

Enlai Guo

Publications and source records attributed to Enlai Guo.

3 recordsLinked to original sources

arXiv2025

Fast Non-Line-of-Sight Transient Data Simulation and an Open Benchmark Dataset

Non-Line-of-Sight (NLOS) imaging reconstructs the shape and depth of hidden objects from picosecond-resolved transient signals, offering potential applications in autonomous driving, security, and medical diagnostics. However, current NLOS experiments rely on expensive hardware and complex system alignment, limiting their scalability. This manuscript presents a simplified simulation method that generates NLOS transient data by modeling light-intensity transport rather than performing conventional path tracing, significantly enhancing computational efficiency. All scene elements, including the relay surface, hidden target, stand-off distance, detector time resolution, and acquisition window are fully parameterized, allowing for rapid configuration of test scenarios. Reconstructions based on the simulated data accurately recover hidden geometries, validating the effectiveness of the approach. The proposed tool reduces the entry barrier for NLOS research and supports the optimization of system design.

physics.optics↗

arXiv2020

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.

eess.IV↗

arXiv2019

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.

eess.IV↗