arXiv subjects

Jixiong Pu

Publications and source records attributed to Jixiong Pu.

6 recordsLinked to original sources

arXiv2026

Correlation invariance unlocks robust calibration-free orbital-angular-momentum multiplexing transmission under dynamic scattering scenarios

Orbital angular momentum (OAM) multiplexing offers a promising approach to high-capacity optical communication by harnessing the orthogonality of vortex beams. However, its practical deployment is severely limited in real-world settings where dynamic scattering media, such as turbulent atmosphere, distort multiplexed fields into random speckles and disrupt OAM demultiplexing. Although existing wavefront shaping and deep learning methods can mitigate static distortions, they fail under time-varying scattering conditions, leading to significant crosstalk and unreliable recovery. Here, we introduce a new concept, correlation invariance, which enables scattering-immune, robust OAM multiplexed transmission through dynamic media. By capturing orthogonally polarized speckle holograms in a compact common-path geometry and computing their intensity cross-correlation, dynamically imposed scattering phases are cancelled out while deterministic object information is preserved. This allows single-shot reconstruction of both amplitude and phase of the input OAM-multiplexed fields, without any pre-calibration or training. As a proof of principle, we demonstrate high-fidelity transmission of 24-bit RGB data with 99.61% accuracy under static scattering and 98.97% accuracy under dynamic scattering. This approach addresses a long-standing barrier in OAM-based systems and opens avenues for robust high-capacity optical communications, encryption, and imaging in dynamic scattering environments.

physics.optics↗

arXiv2026

Real-time Calibration-free Imaging Through Dynamic and Distinct Multimode Fibers via Spatial Harmonic Invariant Nonlinear Encoding (SHINE)

Multimode fibers (MMFs) provide a compact, high-throughput platform for minimally invasive imaging and information transmission. However, their utility is fundamentally constrained by mode mixing, which renders image transmission spatially disrupted and sensitive to external perturbations. Current imaging methods typically rely on transmission matrix measurement or deep learning models that are fragile to fiber movement, necessitating frequent, time-consuming calibrations and re-calibrations that are easily disrupted and fail to generalize across different fiber configurations, let alone across entirely distinct fibers. Here, we propose a calibration and feedback-free MMF coherent imaging paradigm, that we termed Spatial Harmonic Invariant Nonlinear Encoding (SHINE). By leveraging the angle-dependent phase-matching conditions of second-harmonic generation, we encode spatial features into broadband spectral signatures that possess intrinsic insensitivity not only to modal scrambling but also to fiber bending, movement, as well as structural variations. This spectral representation enables a deep learning model to robustly reconstruct images in real time despite dynamic perturbations and even generalizes well to distinct MMFs without recalibration or feedback. We achieve experimentally an average Pearson correlation coefficient (PCC) of 0.82 for image reconstruction tasks on Fashion-MNIST and a classification accuracy of 92.3% on HERLEV biomedical dataset. Uniquely, our method exhibits remarkable cross-fiber generalization: a model trained on a single MMF successfully reconstructs images transmitted through entirely distinct, previously unseen MMFs with a PCC of 0.74. These results establish a robust, calibration-free framework for imaging through MMFs in real time, paving the way for practical, resilient optical diagnostics that operate without distal-end feedback.

physics.optics↗

arXiv2025

Speckle-Driven Single-Shot Orbital Angular Momentum Recognition with Ultra-Low Sampling Density

Orbital angular momentum (OAM) recognition of vortex beams is critical for applications ranging from optical communications to quantum technologies. However, conventional approaches designed for free-space propagation struggle when light passes through scattering media, such as multimode fibers (MMF), and often rely on high-resolution sensors with tens of thousands of pixels to record detailed intensity profiles. Here, by harnessing scattering media as intrinsic encoders rather than detrimental factors, we introduce a speckle-driven OAM recognition technique termed patially multiplexed points detection (SMPD). This method extracts intensity information from a few spatially distributed points in a speckle plane, where object feature is naturally multiplexed, thereby transforming scattering from a detrimental effect into an efficient encoding mechanism while drastically reducing sampling requirements. Remarkably, it achieves over 99% retrieval accuracy for OAMs recognition using just 16 sampling points, corresponding to a sampling density of 0.024% compared with conventional imaging-based approaches. Furthermore, spatiotemporally interleaved vortex beams decoding, highcapacity OAM-multiplexed communication, MNIST, and Fashion-MNIST classification are implemented to verify the versatility of SMPD. This work establishes a scalable strategy for efficient optical information processing and fiberbased sensing in complex environments.

physics.optics↗

arXiv2021

Reconstructing Images of Two Adjacent Objects through Scattering Medium Using Generative Adversarial Network

Reconstruction of image by using convolutional neural networks (CNNs) has been vigorously studied in the last decade. Until now, there have being developed several techniques for imaging of a single object through scattering medium by using neural networks, however how to reconstruct images of more than one object simultaneously seems hard to realize. In this paper, we demonstrate an approach by using generative adversarial network (GAN) to reconstruct images of two adjacent objects through scattering media. We construct an imaging system for imaging of two adjacent objects behind the scattering media. In general, as the light field of two adjacent object images pass through the scattering slab, a speckle pattern is obtained. The designed adversarial network, which is called as YGAN, is employed to reconstruct the images simultaneously. It is shown that based on the trained YGAN, we can reconstruct images of two adjacent objects from one speckle pattern with high fidelity. In addition, we study the influence of the object image types, and the distance between the two adjacent objects on the fidelity of the reconstructed images. Moreover even if another scattering medium is inserted between the two objects, we can also reconstruct the images of two objects from a speckle with high quality. The technique presented in this work can be used for applications in areas of medical image analysis, such as medical image classification, segmentation, and studies of multi-object scattering imaging etc.

eess.IV↗

arXiv2021

Recognizing three-dimensional phase images with deep learning

Optical phase contains key information for biomedical and astronomical imaging. However, it is often obscured by layers of heterogeneous and scattering media, which render optical phase imaging at different depths an utmost challenge. Limited by the memory effect, current methods for phase imaging in strong scattering media are inapplicable to retrieving phases at different depths. To address this challenge, we developed a speckle three-dimensional reconstruction network (STRN) to recognize phase objects behind scattering media, which circumvents the limitations of memory effect. From the single-shot, reference-free and scanning-free speckle pattern input, STRN distinguishes depth-resolving quantitative phase information with high fidelity. Our results promise broad applications in biomedical tomography and endoscopy.

physics.optics↗

arXiv2018

Complex field measurement in a single pixel hybrid correlation holography

We propose a new scheme for recovery of complex valued object in a single-pixel hybrid correlation holography. Idea is to generate an intensity correlation hologram by correlation of the intensity of a single pixel detector and digitally propagated two dimensional intensity patterns. The proposed scheme with theoretical basis is described to reconstruct the objects from a single pixel detector. An experimental arrangement is proposed and as a first step to realize the technique, we have simulated the experimental model for imaging of two different complex objects.

physics.optics↗