arXiv subjects

Liangcai Cao

Publications and source records attributed to Liangcai Cao.

8 recordsLinked to original sources

arXiv2026

Dispersion-engineered meta-coverslip for multimodal synthetic imaging

Multimodal optical imaging provides comprehensive sample characterization but has traditionally been hindered by complex and bulky instrumentation. Here, we propose a dispersion-engineered meta-coverslip to seamlessly integrate bright-field, differential, fluorescence, and holographic imaging modalities within a standard microscope architecture, requiring no hardware modification or realignment. The meta-coverslip utilizes a scalable subwavelength multilayer film to engineer spatio-temporal dispersion for a customized high-dimensional transfer function. As an example, we demonstrate flexible switching among the four imaging modalities by simply tuning the illumination wavelengths. Lastly, we demonstrate that the synthesis of multimodal images can provide spatially registered structural and molecular information for more comprehensive biological analysis. Our approach provides an accessible, scalable, and flexible solution for advanced imaging and is extensible to other multiplexed optical systems in sensing and computing.

physics.optics↗

arXiv2026

Quantitative Decomposition of Speckle Decorrelation for Inverse Problems in Complex Wave Scattering

Optical scattering remains one of the richest phenomena to study in classical optics. Wavefronts that are severely distorted due to scattering still carry significant information about the optical path upstream. Decoding typically starts with the autocorrelation function; yet the precise relationship between it and scatterer statistics still remains unexplored, hindering the model-based method for inverse problems. Here, we reveal that decorrelation due to backward scattering may be quantified as two distinct terms: the first expresses scattering from the surface, whereas the second is due to the volume beneath. The two terms encode higher-order statistics of their respective regimes within the specimen; Experimental studies on representative custom-made scatterers match theoretical predictions with an overall L1-error of less than 0.2%, providing an advanced forward model for various model-based inverse approaches. As a proof-of-concept, we present two examples, scatterer particle size estimation and reconstruction of the incident beam profile, to validate this improvement.

physics.optics↗

arXiv2025

Gaussian splatting holography

In-line holography offers high space-bandwidth product imaging with a simplified lens-free optical system. However, in-line holographic reconstruction is troubled by twin images arising from the Hermitian symmetry of complex fields. Twin images disrupt the reconstruction in solving the ill-posed phase retrieval problem. The known parameters are less than the unknown parameters, causing phase ambiguities. State-of-the-art deep-learning or non-learning methods face challenges in balancing data fidelity with twin-image disturbance. We propose the Gaussian splatting holography (GSH) for twin-image-suppressed holographic reconstruction. GSH uses Gaussian splatting for optical field representation and compresses the number of unknown parameters by a maximum of 15 folds, transforming the original ill-posed phase retrieval into a well-posed one with reduced phase ambiguities. Additionally, the Gaussian splatting tends to form sharp patterns rather than those with noisy twin-image backgrounds as each Gaussian has a spatially slow-varying profile. Experiments show that GSH achieves constraint-free recovery for in-line holography with accuracy comparable to state-of-the-art constraint-based methods, with an average peak signal-to-noise ratio equal to 26 dB, and structure similarity equal to 0.8. Combined with total variation, GSH can be further improved, obtaining a peak signal-to-noise ratio of 31 dB, and a high compression ability of up to 15 folds.

physics.optics↗

arXiv2023

Single-shot deterministic complex amplitude imaging with a single-layer metalens

Conventional imaging systems can only capture light intensity. Meanwhile, the lost phase information may be critical for a variety of applications such as label-free microscopy and optical metrology. Existing phase retrieval techniques typically require a bulky setup, multi-frame measurements, or prior information of the target scene. Here, we proposed an extremely compact system for complex amplitude imaging, leveraging the extreme versatility of a single-layer metalens to generate spatially-multiplexed and polarization-phase-shifted point spread functions. Combining the metalens with a polarization camera, the system can simultaneously record four polarization shearing interference patterns along both in-plane directions, thus allowing the deterministic reconstruction of the complex amplitude light field in a single shot. Using an incoherent light-emitting diode as the illumination, we experimentally demonstrated speckle-noise-free complex amplitude imaging for both static and moving objects with tailored magnification ratio and field-of-view. The miniaturized and robust system may open the door for complex amplitude imaging in portable devices for point-of-care applications.

physics.optics↗

arXiv2022

Quantitative phase imaging through an ultra-thin lensless fiber endoscope

Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging of biomedical samples with up to 1 μm lateral resolution and nanoscale axial resolution. The incident complex light field at the measurement side is precisely reconstructed from a single-shot far-field speckle pattern at the detection side, enabling digital focusing and 3D volumetric reconstruction without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, 3D imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications.

physics.optics↗

arXiv2022

Learned end-to-end high-resolution lensless fiber imaging toward intraoperative real-time cancer diagnosis

Endomicroscopy is indispensable for minimally invasive diagnostics in clinical practice. For optical keyhole monitoring of surgical interventions, high-resolution fiber endoscopic imaging is considered to be very promising, especially in combination with label-free imaging techniques to realize in vivo diagnosis. However, the inherent honeycomb-artifacts of coherent fiber bundles (CFB) reduce the resolution and limit the clinical applications. We propose an end-to-end lensless fiber imaging scheme toward intraoperative real-time cancer diagnosis. The framework includes resolution enhancement and classification networks that use single-shot fiber bundle images to provide both high-resolution images and tumor diagnosis result. The well-trained resolution enhancement network not only recovers high-resolution features beyond the physical limitations of CFB, but also helps improving tumor recognition rate. Especially for glioblastoma, the resolution enhancement network helps increasing the classification accuracy from 90.8% to 95.6%. The novel technique can enable histological real-time imaging through lensless fiber endoscopy and is promising for rapid and minimal-invasive intraoperative diagnosis in clinics.

physics.med-ph↗

arXiv2021

Lensless multicore-fiber microendoscope for real-time tailored light field generation with phase encoder neural network (CoreNet)

The generation of tailored light with multi-core fiber (MCF) lensless microendoscopes is widely used in biomedicine. However, the computer-generated holograms (CGHs) used for such applications are typically generated by iterative algorithms, which demand high computation effort, limiting advanced applications like in vivo optogenetic stimulation and fiber-optic cell manipulation. The random and discrete distribution of the fiber cores induces strong spatial aliasing to the CGHs, hence, an approach that can rapidly generate tailored CGHs for MCFs is highly demanded. We demonstrate a novel phase encoder deep neural network (CoreNet), which can generate accurate tailored CGHs for MCFs at a near video-rate. Simulations show that CoreNet can speed up the computation time by two magnitudes and increase the fidelity of the generated light field compared to the conventional CGH techniques. For the first time, real-time generated tailored CGHs are on-the-fly loaded to the phase-only SLM for dynamic light fields generation through the MCF microendoscope in experiments. This paves the avenue for real-time cell rotation and several further applications that require real-time high-fidelity light delivery in biomedicine.

physics.optics↗

arXiv2021

A Complex Constrained Total Variation Image Denoising Algorithm with Application to Phase Retrieval

This paper considers the constrained total variation (TV) denoising problem for complex-valued images. We extend the definition of TV seminorms for real-valued images to dealing with complex-valued ones. In particular, we introduce two types of complex TV in both isotropic and anisotropic forms. To solve the constrained denoising problem, we adopt a dual approach and derive an accelerated gradient projection algorithm. We further generalize the proposed denoising algorithm as a key building block of the proximal gradient scheme to solve a vast class of complex constrained optimization problems with TV regularizers. As an example, we apply the proposed algorithmic framework to phase retrieval. We combine the complex TV regularizer with the conventional projection-based method within the constraint complex TV model. Initial results from both simulated and optical experiments demonstrate the validity of the constrained TV model in extracting sparsity priors within complex-valued images, while also utilizing physically tractable constraints that help speed up convergence.

eess.IV↗