arXiv subjects

Tianyu Song

Publications and source records attributed to Tianyu Song.

At least 19 recordsLinked to original sources

arXiv2026

BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations

Most work in audio enhancement targets human speech, while bioacoustics is less studied due to noisy recordings and the distinct traits of animal sounds. To fill this gap, we adapt speech enhancement methods and build BioSEN, a model made for bioacoustic signals. BioSEN has three modules: a multi-scale dual-axis attention unit for time-frequency feature extraction, a bio-harmonic multi-scale enhancement unit for capturing harmonic structures, and an energy-adaptive gating connection unit that uses frequency weights to keep vocalizations from being removed as noise. Tests on three bioacoustic datasets show that BioSEN matches or exceeds state-of-the-art speech enhancement models while using far less computation. These results show BioSEN's strength for bioacoustic audio enhancement and its promise for biodiversity monitoring and conservation.

cs.SD↗

arXiv2026

Feasibility of Augmented Reality-Guided Robotic Ultrasound with Cone-Beam CT Integration for Spine Procedures

Accurate needle placement in spine interventions is critical for effective pain management, yet it depends on reliable identification of anatomical landmarks and careful trajectory planning. Conventional imaging guidance often relies both on CT and X-ray fluoroscopy, exposing patients and staff to high dose of radiation while providing limited real-time 3D feedback. We present an optical see-through augmented reality (OST-AR)-guided robotic system for spine procedures that provides in situ visualization of spinal structures to support needle trajectory planning. We integrate a cone-beam CT (CBCT)-derived 3D spine model which is co-registered with live ultrasound, enabling users to combine global anatomical context with local, real-time imaging. We evaluated the system in a phantom user study involving two representative spine procedures: facet joint injection and lumbar puncture. Sixteen participants performed insertions under two visualization conditions: conventional screen vs. AR. Results show that AR significantly reduces execution time and across-task placement error, while also improving usability, trust, and spatial understanding and lowering cognitive workload. These findings demonstrate the feasibility of AR-guided robotic ultrasound for spine interventions, highlighting its potential to enhance accuracy, efficiency, and user experience in image-guided procedures.

cs.HC↗

arXiv2026

Ultrasound-Guided Real-Time Spinal Motion Visualization for Spinal Instability Assessment

Purpose: Spinal instability is a widespread condition that causes pain, fatigue, and restricted mobility, profoundly affecting patients' quality of life. In clinical practice, the gold standard for diagnosis is dynamic X-ray imaging. However, X-ray provides only 2D motion information, while 3D modalities such as computed tomography (CT) or cone beam computed tomography (CBCT) cannot efficiently capture motion. Therefore, there is a need for a system capable of visualizing real-time 3D spinal motion while minimizing radiation exposure. Methods: We propose ultrasound as an auxiliary modality for 3D spine visualization. Due to acoustic limitations, ultrasound captures only the superficial spinal surface. Therefore, the partially compounded ultrasound volume is registered to preoperative 3D imaging. In this study, CBCT provides the neutral spine configuration, while robotic ultrasound acquisition is performed at maximal spinal bending. A kinematic model is applied to the CBCT-derived spine model for coarse registration, followed by ICP for fine registration, with kinematic parameters optimized based on the registration results. Real-time ultrasound motion tracking is then used to estimate continuous 3D spinal motion by interpolating between the neutral and maximally bent states. Results: The pipeline was evaluated on a bendable 3D-printed lumbar spine phantom. The registration error was $1.941 \pm 0.199$ mm and the interpolated spinal motion error was $2.01 \pm 0.309$ mm (median). Conclusion: The proposed robotic ultrasound framework enables radiation-reduced, real-time 3D visualization of spinal motion, offering a promising 3D alternative to conventional dynamic X-ray imaging for assessing spinal instability.

physics.med-ph↗

arXiv2026

Comparative Study of Ultrasound Shape Completion and CBCT-Based AR Workflows for Spinal Needle Interventions

Purpose: This study compares two augmented reality (AR)-guided imaging workflows, one based on ultrasound shape completion and the other on cone-beam computed tomography (CBCT), for planning and executing lumbar needle interventions. The aim is to assess how imaging modality influences user performance, usability, and trust during AR-assisted spinal procedures. Methods: Both imaging systems were integrated into an AR framework, enabling in situ visualization and trajectory guidance. The ultrasound-based workflow combined AR-guided robotic scanning, probabilistic shape completion, and AR visualization. The CBCT-based workflow used AR-assisted scan volume planning, CBCT acquisition, and AR visualization. A between-subject user study was conducted and evaluated in two phases: (1) planning and image acquisition, and (2) needle insertion. Results: Planning time was significantly shorter with the CBCT-based workflow, while SUS, SEQ, and NASA-TLX were comparable between modalities. In the needle insertion phase, the CBCT-based workflow yielded marginally faster insertion times, lower placement error, and better subjective ratings with higher Trust. The ultrasound-based workflow achieved adequate accuracy for facet joint insertion, but showed larger errors for lumbar puncture, where reconstructions depended more heavily on shape completion. Conclusion: The findings indicate that both AR-guided imaging pipelines are viable for spinal intervention support. CBCT-based AR offers advantages in efficiency, precision, usability, and user confidence during insertion, whereas ultrasound-based AR provides adaptive, radiation-free imaging but is limited by shape completion in deeper spinal regions. These complementary characteristics motivate hybrid AR guidance that uses CBCT for global anatomical context and planning, augmented by ultrasound for adaptive intraoperative updates.

cs.HC↗

arXiv2025

Rethinking Nighttime Image Deraining via Learnable Color Space Transformation

Compared to daytime image deraining, nighttime image deraining poses significant challenges due to inherent complexities of nighttime scenarios and the lack of high-quality datasets that accurately represent the coupling effect between rain and illumination. In this paper, we rethink the task of nighttime image deraining and contribute a new high-quality benchmark, HQ-NightRain, which offers higher harmony and realism compared to existing datasets. In addition, we develop an effective Color Space Transformation Network (CST-Net) for better removing complex rain from nighttime scenes. Specifically, we propose a learnable color space converter (CSC) to better facilitate rain removal in the Y channel, as nighttime rain is more pronounced in the Y channel compared to the RGB color space. To capture illumination information for guiding nighttime deraining, implicit illumination guidance is introduced enabling the learned features to improve the model's robustness in complex scenarios. Extensive experiments show the value of our dataset and the effectiveness of our method. The source code and datasets are available at https://github.com/guanqiyuan/CST-Net.

cs.CV↗

arXiv2025

WeatherBench: A Real-World Benchmark Dataset for All-in-One Adverse Weather Image Restoration

Existing all-in-one image restoration approaches, which aim to handle multiple weather degradations within a single framework, are predominantly trained and evaluated using mixed single-weather synthetic datasets. However, these datasets often differ significantly in resolution, style, and domain characteristics, leading to substantial domain gaps that hinder the development and fair evaluation of unified models. Furthermore, the lack of a large-scale, real-world all-in-one weather restoration dataset remains a critical bottleneck in advancing this field. To address these limitations, we present a real-world all-in-one adverse weather image restoration benchmark dataset, which contains image pairs captured under various weather conditions, including rain, snow, and haze, as well as diverse outdoor scenes and illumination settings. The resulting dataset provides precisely aligned degraded and clean images, enabling supervised learning and rigorous evaluation. We conduct comprehensive experiments by benchmarking a variety of task-specific, task-general, and all-in-one restoration methods on our dataset. Our dataset offers a valuable foundation for advancing robust and practical all-in-one image restoration in real-world scenarios. The dataset has been publicly released and is available at https://github.com/guanqiyuan/WeatherBench.

cs.CV↗

arXiv2025

Towards High-Fidelity and Controllable Bioacoustic Generation via Enhanced Diffusion Learning

Generative modeling offers new opportunities for bioacoustics, enabling the synthesis of realistic animal vocalizations that could support biomonitoring efforts and supplement scarce data for endangered species. However, directly generating bird call waveforms from noisy field recordings remains a major challenge. We propose BirdDiff, a generative framework designed to synthesize bird calls from a noisy dataset of 12 wild bird species. The model incorporates a "zeroth layer" stage for multi-scale adaptive bird-call enhancement, followed by a diffusion-based generator conditioned on three modalities: Mel-frequency cepstral coefficients, species labels, and textual descriptions. The enhancement stage improves signal-to-noise ratio (SNR) while minimizing spectral distortion, achieving the highest SNR gain (+10.45 dB) and lowest Itakura-Saito Distance (0.54) compared to three widely used non-training enhancement methods. We evaluate BirdDiff against a baseline generative model, DiffWave. Our method yields substantial improvements in generative quality metrics: Fr\'echet Audio Distance (0.590 to 0.213), Jensen-Shannon Divergence (0.259 to 0.226), and Number of Statistically-Different Bins (7.33 to 5.58). To assess species-specific detail preservation, we use a ResNet50 classifier trained on the original dataset to identify generated samples. Classification accuracy improves from 35.9% (DiffWave) to 70.1% (BirdDiff), with 8 of 12 species exceeding 70% accuracy. These results demonstrate that BirdDiff enables high-fidelity, controllable bird call generation directly from noisy field recordings.

cs.SD↗

arXiv2025

Deep Learning for Automated Identification of Vietnamese Timber Species: A Tool for Ecological Monitoring and Conservation

Accurate identification of wood species plays a critical role in ecological monitoring, biodiversity conservation, and sustainable forest management. Traditional classification approaches relying on macroscopic and microscopic inspection are labor-intensive and require expert knowledge. In this study, we explore the application of deep learning to automate the classification of ten wood species commonly found in Vietnam. A custom image dataset was constructed from field-collected wood samples, and five state-of-the-art convolutional neural network architectures--ResNet50, EfficientNet, MobileViT, MobileNetV3, and ShuffleNetV2--were evaluated. Among these, ShuffleNetV2 achieved the best balance between classification performance and computational efficiency, with an average accuracy of 99.29\% and F1-score of 99.35\% over 20 independent runs. These results demonstrate the potential of lightweight deep learning models for real-time, high-accuracy species identification in resource-constrained environments. Our work contributes to the growing field of ecological informatics by providing scalable, image-based solutions for automated wood classification and forest biodiversity assessment.

cs.CV↗

arXiv2025

Stochastic forest transition model dynamics and parameter estimation via deep learning

Forest transitions, characterized by dynamic shifts between forest, agricultural, and abandoned lands, are complex phenomena. This study developed a stochastic differential equation model to capture the intricate dynamics of these transitions. We established the existence of global positive solutions for the model and conducted numerical analyses to assess the impact of model parameters on deforestation incentives. To address the challenge of parameter estimation, we proposed a novel deep learning approach that estimates all model parameters from a single sample containing time-series observations of forest and agricultural land proportions. This innovative approach enables us to understand forest transition dynamics and deforestation trends at any future time.

stat.ML↗

arXiv2025

Intelligent Virtual Sonographer (IVS): Enhancing Physician-Robot-Patient Communication

The advancement and maturity of large language models (LLMs) and robotics have unlocked vast potential for human-computer interaction, particularly in the field of robotic ultrasound. While existing research primarily focuses on either patient-robot or physician-robot interaction, the role of an intelligent virtual sonographer (IVS) bridging physician-robot-patient communication remains underexplored. This work introduces a conversational virtual agent in Extended Reality (XR) that facilitates real-time interaction between physicians, a robotic ultrasound system(RUS), and patients. The IVS agent communicates with physicians in a professional manner while offering empathetic explanations and reassurance to patients. Furthermore, it actively controls the RUS by executing physician commands and transparently relays these actions to the patient. By integrating LLM-powered dialogue with speech-to-text, text-to-speech, and robotic control, our system enhances the efficiency, clarity, and accessibility of robotic ultrasound acquisition. This work constitutes a first step toward understanding how IVS can bridge communication gaps in physician-robot-patient interaction, providing more control and therefore trust into physician-robot interaction while improving patient experience and acceptance of robotic ultrasound.

cs.HC↗

arXiv2025

Enhancing Patient Acceptance of Robotic Ultrasound through Conversational Virtual Agent and Immersive Visualizations

Robotic ultrasound systems can enhance medical diagnostics, but patient acceptance is a challenge. We propose a system combining an AI-powered conversational virtual agent with three mixed reality visualizations to improve trust and comfort. The virtual agent, powered by a large language model, engages in natural conversations and guides the ultrasound robot, enhancing interaction reliability. The visualizations include augmented reality, augmented virtuality, and fully immersive virtual reality, each designed to create patient-friendly experiences. A user study demonstrated significant improvements in trust and acceptance, offering valuable insights for designing mixed reality and virtual agents in autonomous medical procedures.

cs.HC↗

arXiv2024

Learning A Spiking Neural Network for Efficient Image Deraining

Recently, spiking neural networks (SNNs) have demonstrated substantial potential in computer vision tasks. In this paper, we present an Efficient Spiking Deraining Network, called ESDNet. Our work is motivated by the observation that rain pixel values will lead to a more pronounced intensity of spike signals in SNNs. However, directly applying deep SNNs to image deraining task still remains a significant challenge. This is attributed to the information loss and training difficulties that arise from discrete binary activation and complex spatio-temporal dynamics. To this end, we develop a spiking residual block to convert the input into spike signals, then adaptively optimize the membrane potential by introducing attention weights to adjust spike responses in a data-driven manner, alleviating information loss caused by discrete binary activation. By this way, our ESDNet can effectively detect and analyze the characteristics of rain streaks by learning their fluctuations. This also enables better guidance for the deraining process and facilitates high-quality image reconstruction. Instead of relying on the ANN-SNN conversion strategy, we introduce a gradient proxy strategy to directly train the model for overcoming the challenge of training. Experimental results show that our approach gains comparable performance against ANN-based methods while reducing energy consumption by 54%. The code source is available at https://github.com/MingTian99/ESDNet.

cs.CV↗

arXiv2023

MPSA-DenseNet: A novel deep learning model for English accent classification

This paper presents three innovative deep learning models for English accent classification: Multi-DenseNet, PSA-DenseNet, and MPSE-DenseNet, that combine multi-task learning and the PSA module attention mechanism with DenseNet. We applied these models to data collected from six dialects of English across native English speaking regions (Britain, the United States, Scotland) and nonnative English speaking regions (China, Germany, India). Our experimental results show a significant improvement in classification accuracy, particularly with MPSA-DenseNet, which outperforms all other models, including DenseNet and EPSA models previously used for accent identification. Our findings indicate that MPSA-DenseNet is a highly promising model for accurately identifying English accents.

cs.CL↗

arXiv2022

STTAR: Surgical Tool Tracking using off-the-shelf Augmented Reality Head-Mounted Displays

The use of Augmented Reality (AR) for navigation purposes has shown beneficial in assisting physicians during the performance of surgical procedures. These applications commonly require knowing the pose of surgical tools and patients to provide visual information that surgeons can use during the task performance. Existing medical-grade tracking systems use infrared cameras placed inside the Operating Room (OR) to identify retro-reflective markers attached to objects of interest and compute their pose. Some commercially available AR Head-Mounted Displays (HMDs) use similar cameras for self-localization, hand tracking, and estimating the objects' depth. This work presents a framework that uses the built-in cameras of AR HMDs to enable accurate tracking of retro-reflective markers, such as those used in surgical procedures, without the need to integrate any additional components. This framework is also capable of simultaneously tracking multiple tools. Our results show that the tracking and detection of the markers can be achieved with an accuracy of 0.09 +- 0.06 mm on lateral translation, 0.42 +- 0.32 mm on longitudinal translation, and 0.80 +- 0.39 deg for rotations around the vertical axis. Furthermore, to showcase the relevance of the proposed framework, we evaluate the system's performance in the context of surgical procedures. This use case was designed to replicate the scenarios of k-wire insertions in orthopedic procedures. For evaluation, two surgeons and one biomedical researcher were provided with visual navigation, each performing 21 injections. Results from this use case provide comparable accuracy to those reported in the literature for AR-based navigation procedures.

cs.RO↗

arXiv2020

Reflective-AR Display: An Interaction Methodology for Virtual-Real Alignment in Medical Robotics

Robot-assisted minimally invasive surgery has shown to improve patient outcomes, as well as reduce complications and recovery time for several clinical applications. While increasingly configurable robotic arms can maximize reach and avoid collisions in cluttered environments, positioning them appropriately during surgery is complicated because safety regulations prevent automatic driving. We propose a head-mounted display (HMD) based augmented reality (AR) system designed to guide optimal surgical arm set up. The staff equipped with HMD aligns the robot with its planned virtual counterpart. In this user-centric setting, the main challenge is the perspective ambiguities hindering such collaborative robotic solution. To overcome this challenge, we introduce a novel registration concept for intuitive alignment of AR content to its physical counterpart by providing a multi-view AR experience via reflective-AR displays that simultaneously show the augmentations from multiple viewpoints. Using this system, users can visualize different perspectives while actively adjusting the pose to determine the registration transformation that most closely superimposes the virtual onto the real. The experimental results demonstrate improvement in the interactive alignment of a virtual and real robot when using a reflective-AR display. We also present measurements from configuring a robotic manipulator in a simulated trocar placement surgery using the AR guidance methodology.

cs.RO↗

arXiv2020

Spatiotemporal-Aware Augmented Reality: Redefining HCI in Image-Guided Therapy

Suboptimal interaction with patient data and challenges in mastering 3D anatomy based on ill-posed 2D interventional images are essential concerns in image-guided therapies. Augmented reality (AR) has been introduced in the operating rooms in the last decade; however, in image-guided interventions, it has often only been considered as a visualization device improving traditional workflows. As a consequence, the technology is gaining minimum maturity that it requires to redefine new procedures, user interfaces, and interactions. The main contribution of this paper is to reveal how exemplary workflows are redefined by taking full advantage of head-mounted displays when entirely co-registered with the imaging system at all times. The proposed AR landscape is enabled by co-localizing the users and the imaging devices via the operating room environment and exploiting all involved frustums to move spatial information between different bodies. The awareness of the system from the geometric and physical characteristics of X-ray imaging allows the redefinition of different human-machine interfaces. We demonstrate that this AR paradigm is generic, and can benefit a wide variety of procedures. Our system achieved an error of $4.76\pm2.91$ mm for placing K-wire in a fracture management procedure, and yielded errors of $1.57\pm1.16^\circ$ and $1.46\pm1.00^\circ$ in the abduction and anteversion angles, respectively, for total hip arthroplasty. We hope that our holistic approach towards improving the interface of surgery not only augments the surgeon's capabilities but also augments the surgical team's experience in carrying out an effective intervention with reduced complications and provide novel approaches of documenting procedures for training purposes.

cs.CV↗

arXiv2019

LumiPath -- Towards Real-time Physically-based Rendering on Embedded Devices

With the increasing computational power of today's workstations, real-time physically-based rendering is within reach, rapidly gaining attention across a variety of domains. These have expeditiously applied to medicine, where it is a powerful tool for intuitive 3D data visualization. Embedded devices such as optical see-through head-mounted displays (OST HMDs) have been a trend for medical augmented reality. However, leveraging the obvious benefits of physically-based rendering remains challenging on these devices because of limited computational power, memory usage, and power consumption. We navigate the compromise between device limitations and image quality to achieve reasonable rendering results by introducing a novel light field that can be sampled in real-time on embedded devices. We demonstrate its applications in medicine and discuss limitations of the proposed method. An open-source version of this project is available at https://github.com/lorafib/LumiPath which provides full insight on implementation and exemplary demonstrational material.

cs.CV↗

arXiv2017

Background Radiation Cancellation for Free-Space Optical Communications with IM/DD

Besides atmospheric turbulence and pointing errors which cause the signal intensity fluctuation, background radiation also impairs the free-space optical intensity-modulation / direct-detection link performance by introducing a noisy photocurrent component in the receiver. Methods such as adopting some specific optics systems, using pilot symbols or line codes to estimate the background information and cancel it accordingly, and pulse-position modulation (PPM), can be adopted to mitigate the impact of background radiation. However, purely depending on the optics system, the background radiation can only be reduced, but not completely cancelled; and the use of any of pilot symbols, line codes and PPM reduces the system spectral efficiency drastically. In this paper, based on the generalized likelihood ratio test (GLRT) principle, we develop a Viterbi-type trellis-search sequence receiver (the GLRT sequence receiver) that can estimate the unknown channel gain and the background radiation simultaneously, and detect the data sequence accordingly. This receiver requires very few pilot symbols, and therefore, does not significantly reduce the bandwidth efficiency. Its error performance can approach that of detection with perfect information of the channel state and the background radiation, as the observation window length used for forming the decision metric increases. Since a Viterbi-type trellis-search algorithm is adopted, the search complexity is very low and is independent of the observation window size. However, the search complexity of the GLRT sequence receiver grows exponentially with the modulation order. To further simplify the implementation, we derive a more efficient decision-feedback symbol-by-symbol receiver which retains the same error performance as that of the GLRT sequence receiver.

cs.IT↗