arXiv subjects

Jiahe Cui

Publications and source records attributed to Jiahe Cui.

9 recordsLinked to original sources

arXiv2025

Full vectorial field sensing using liquid crystal droplet arrays

Determining the amplitude, phase, and polarization profile of light is essential for both fundamental scientific discovery and applications spanning optical metrology, microscopy, astronomy, and optical communication/computing technologies. However, most modern measurement approaches are unable to retrieve such parameters readily, often relying on bulky and expensive hardware, or lacking the capability for single-shot sensing. Here, we introduce a low cost, compact, full vectorial field sensor based on an inkjet-printed nematic liquid crystal droplet array that enables simultaneous measurement of these important characteristics of light. Polarization and intensity are measured via division-of-wavefront polarimetry, exploiting the droplets' spatially varying birefringence, while the phase is reconstructed by treating each droplet as a separate microlens in a Shack-Hartmann-like wavefront sensor configuration. To demonstrate the system's performance, we characterize aberrated dual-wavelength beams carrying distinct intensity, phase, and polarization information, confirming accurate retrieval of the optical field profiles for both spectral components.

physics.optics↗

arXiv2024

Intensity adaptive optics

Adaptive optics (AO) is a powerful tool employed across various research fields, from aerospace to microscopy. Traditionally, AO has focused on correcting optical phase aberrations, with recent advances extending to polarisation compensation. However, intensity errors are also prevalent in optical systems, yet effective correction methods are still in their infancy. Here, we introduce a novel AO approach, termed intensity adaptive optics (I-AO), which employs a dual-feedback loop mechanism to first address non-uniform intensity distribution and subsequently compensate for energy loss at the pupil plane. We demonstrate that I-AO can operate in both sensor-based and sensorless formats and validate its feasibility by quantitatively analysing the focus quality of an aberrated system. This technique expands the AO toolkit, paving the way for next-generation AO technology.

physics.optics↗

arXiv2023

Vectorial adaptive optics for advanced imaging systems

Vectorial adaptive optics (V-AO) is a cutting-edge technique extending conventional AO into the vectorial domain encompassing both polarization and phase feedback correction for optical systems. However, previous V-AO approaches focus on point correction. In this letter, we extend this AO approach into the imaging domain. We show how V-AO can benefit an aberrated imaging system to enhance not only scalar imaging but also the quality of vectorial information. Two important criteria, vectorial precision and uniformity are put forward and used in practice to evaluate the performance of the correction. These experimental validations pave the way for real-world imaging for V-AO technology and its applications.

physics.optics↗

arXiv2023

A reconfigurable arbitrary retarder array as complex structured matter

Tuneable retarder arrays, such as spatially patterned liquid crystal devices, have given rise to impressive photonic functionality, fuelling diverse applications ranging from microscopy and holography to encryption and communications. Presently these solutions are limited by the controllable degrees of freedom of structured matter, hindering applications that demand photonic systems with high flexibility and reconfigurable topologies. Here we demonstrate a compound modulator that implements a synthetic tuneable arbitrary retarder array as virtual pixels derived by cascading low functionality tuneable devices, realising full dynamic control of its arbitrary elliptical axis geometry, retardance value, and induced phase. Our approach offers unprecedented functionality that is user-defined and possesses high flexibility, allowing our modulator to act as a new beam generator, analyser, and corrector, opening an exciting path to tuneable topologies of light and matter.

physics.optics↗

arXiv2023

MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement Learning

Manga is a fashionable Japanese-style comic form that is composed of black-and-white strokes and is generally displayed as raster images on digital devices. Typical mangas have simple textures, wide lines, and few color gradients, which are vectorizable natures to enjoy the merits of vector graphics, e.g., adaptive resolutions and small file sizes. In this paper, we propose MARVEL (MAnga's Raster to VEctor Learning), a primitive-wise approach for vectorizing raster mangas by Deep Reinforcement Learning (DRL). Unlike previous learning-based methods which predict vector parameters for an entire image, MARVEL introduces a new perspective that regards an entire manga as a collection of basic primitives\textemdash stroke lines, and designs a DRL model to decompose the target image into a primitive sequence for achieving accurate vectorization. To improve vectorization accuracies and decrease file sizes, we further propose a stroke accuracy reward to predict accurate stroke lines, and a pruning mechanism to avoid generating erroneous and repeated strokes. Extensive subjective and objective experiments show that our MARVEL can generate impressive results and reaches the state-of-the-art level. Our code is open-source at: https://github.com/SwordHolderSH/Mang2Vec.

cs.CV↗

arXiv2022

Full Poincaré polarimetry enabled through physical inference

While polarisation sensing is vital in many areas of research, with applications spanning from microscopy to aerospace, traditional approaches are limited by method-related error amplification or accumulation, placing fundamental limitations on precision and accuracy in single-shot polarimetry. Here, we put forward a new measurement paradigm to circumvent this, introducing the notion of a universal full Poincaré generator to map all polarisation analyser states into a single vectorially structured light field, allowing all vector components to be analysed in a single-shot with theoretically user-defined precision. To demonstrate the advantage of our approach, we use a common GRIN optic as our mapping device and show mean errors of <1% for each vector component, enhancing the sensitivity by around three times, allowing us to sense weak polarisation aberrations not measurable by traditional single-shot techniques. Our work paves the way for next-generation polarimetry, impacting a wide variety of applications relying on weak vector measurement.

physics.optics↗

arXiv2021

SenAOReFoc: A Closed-Loop Sensorbased Adaptive Optics and Remote Focusing Control Software

SenAOReFoc is a closed-loop sensorbased adaptive optics (AO) and remote focusing control software that works with a deformable mirror (DM) and a Shack-Hartmann wavefront sensor (SHWS). It is programmed in Python and is open-source on Github https://github.com/jiahecui/SenAOReFoc. A detailed user guide can be found in the Github repository. Here, we give a brief summary of basic software functionalities, a statement of need, and some examples for the usage of this software.

physics.ins-det↗

arXiv2020

MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing

Manga is a world popular comic form originated in Japan, which typically employs black-and-white stroke lines and geometric exaggeration to describe humans' appearances, poses, and actions. In this paper, we propose MangaGAN, the first method based on Generative Adversarial Network (GAN) for unpaired photo-to-manga translation. Inspired by how experienced manga artists draw manga, MangaGAN generates the geometric features of manga face by a designed GAN model and delicately translates each facial region into the manga domain by a tailored multi-GANs architecture. For training MangaGAN, we construct a new dataset collected from a popular manga work, containing manga facial features, landmarks, bodies, and so on. Moreover, to produce high-quality manga faces, we further propose a structural smoothing loss to smooth stroke-lines and avoid noisy pixels, and a similarity preserving module to improve the similarity between domains of photo and manga. Extensive experiments show that MangaGAN can produce high-quality manga faces which preserve both the facial similarity and a popular manga style, and outperforms other related state-of-the-art methods.

cs.CV↗

arXiv2020

ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems

Recently, the rapid development of Solid-State LiDAR (SSL) enables low-cost and efficient obtainment of 3D point clouds from the environment, which has inspired a large quantity of studies and applications. However, the non-uniformity of its scanning pattern, and the inconsistency of the ranging error distribution bring challenges to its calibration task. In this paper, we proposed a fully automatic calibration method for the non-repetitive scanning SSL and camera systems. First, a temporal-spatial-based geometric feature refinement method is presented, to extract effective features from SSL point clouds; then, the 3D corners of the calibration target (a printed checkerboard) are estimated with the reflectance distribution of points. Based on the above, a target-based extrinsic calibration method is finally proposed. We evaluate the proposed method on different types of LiDAR and camera sensor combinations in real conditions, and achieve accuracy and robustness calibration results. The code is available at https://github.com/HViktorTsoi/ACSC.git .

cs.CV↗