arXiv subjects

Xibo Wang

Publications and source records attributed to Xibo Wang.

4 recordsLinked to original sources

arXiv2026

Numerical speckle reduction for optical coherence tomography based on an analytical image formation model

Speckle is an intrinsic granular pattern that limits the effective resolution of optical coherence tomography (OCT). We propose a numerical speckle reduction method using the "real part of a shifted complex conjugate product" (real-SCCP), which is a new image representation designed based on an analytical imaging model of OCT. In the real-SCCP image representation, the speckle can be fully numerically modulated after OCT signal acquisition. By averaging multiple SCCP images with different speckle realizations, the speckle is effectively reduced. Furthermore, this method enables clear visualization of structures in tumor spheroids and zebrafish eyes, and significantly outperforms conventional frame-averaging-based speckle reduction techniques.

physics.optics↗

arXiv2026

RPS: Information Elicitation with Reinforcement Prompt Selection

Large language models (LLMs) have shown remarkable capabilities in dialogue generation and reasoning, yet their effectiveness in eliciting user-known but concealed information in open-ended conversations remains limited. In many interactive AI applications, such as personal assistants, tutoring systems, and legal or clinical support, users often withhold sensitive or uncertain information due to privacy concerns, ambiguity, or social hesitation. This makes it challenging for LLMs to gather complete and contextually relevant inputs. In this work, we define the problem of information elicitation in open-ended dialogue settings and propose Reinforcement Prompt Selection (RPS), a lightweight reinforcement learning framework that formulates prompt selection as a sequential decision-making problem. To analyze this problem in a controlled setting, we design a synthetic experiment, where a reinforcement learning agent outperforms a random query baseline, illustrating the potential of policy-based approaches for adaptive information elicitation. Building on this insight, RPS learns a policy over a pool of prompts to adaptively elicit concealed or incompletely expressed information from users through dialogue. We also introduce IELegal, a new benchmark dataset constructed from real legal case documents, which simulates dialogue-based information elicitation tasks aimed at uncovering case-relevant facts. In this setting, RPS outperforms static prompt baselines, demonstrating the effectiveness of adaptive prompt selection for eliciting critical information in LLM-driven dialogue systems.

cs.LG↗

arXiv2026

Neural-network-based high-speed and high-definition full-field dynamic optical coherence tomography

A neural-network (NN)-based method for high-speed, high-definition dynamic optical coherence tomography (DOCT) using full-field swept-source optical coherence microscopy (FF-SS-OCM) is demonstrated. FF-SS-OCM provides high-definition OCT images, but, particularly in DOCT imaging, it results in a significant enlargement of the data size and subsequently long data streaming and processing time, which prevents high-throughput imaging. We address this issue by introducing an NN-based DOCT method that generates high-definition logarithmic intensity variance (LIV) -based DOCT images from only four OCT volumes, whereas the conventional method required 32 volumes. The NN model successfully generates an LIV image that is qualitatively and quantitatively similar to the LIV image computed from 32 volumes. This approach significantly reduces data size, transfer time, and processing time for DOCT imaging by a factor of eight. Specifically, these were reduced from 42 GB to 5.3 GB, 7 min to 55 s, and 4 hours to 30 min, respectively.

physics.optics↗

arXiv2025

Dynamic full-field swept-source optical coherence microscope for cellular-resolution, long-depth, and intratissue-activity imaging

Optical coherence tomography (OCT) microscope (OCM) uses a high-numerical-aperture objective to achieve cellular-level lateral resolution. However, its practical imaging depth range is limited by the depth of focus (DOF). Although computational refocusing can potentially provide sharp images outside the DOF, signal reduction by the confocal effect still limits the imaging depth in practice in point-scanning OCT. In addition, standard OCT cannot visualize intra-tissue activities. To overcome these limitations, we demonstrated a spatially coherent full-field OCM (SC-FFOCM) with computational refocusing. In addition, a repetitive acquisition protocol was designed to visualize intra-tissue activities (i.e., dynamic OCT imaging). The in-focus lateral resolution is 1.4 um, and the axial resolution is 6.5 um (in air) at full-width at half-maximum intensity. Three-dimensional structure and the dynamic OCT imaging using SC-FFOCM with computational refocusing was applied to human breast adenocarcinoma spheroids (MCF-7 cell line). Volumetric dynamic imaging with cellular-level lateral resolution was demonstrated over the full depth of the spheroid.

physics.optics↗