arXiv subjects

Wenfei Zhang

Publications and source records attributed to Wenfei Zhang.

7 recordsLinked to original sources

arXiv2026

Active control of the peak value of the Hanbury Brown-Twiss effect using coherent light by lensless holographic projection

Computer-generated holography enables projection of target patterns onto designated planes, providing deterministic control over the probability density function of the projected light intensity. Here, we introduce an active control scheme for the peak value of the Hanbury Brown--Twiss effect, $g^{(2)}(0)$, utilizing lensless holographic projection with coherent light. Notably, single-frame holographic projection yields a markedly different $g^{(2)}(0)$ from its multiframe-averaged counterpart due to the presence of coherent speckle noise. With the coherent speckle noise suppression, we derive an analytical expression $g^{(2)}(0)$ on holographic projection plane, revealing that it is determined by the target coherence length, its statistics, and the numerical aperture of projection system. Our experimental results show good agreement with the theoretical analysis, confirming the joint influence of these factors. By employing dynamic sparse target patterns, we achieve a maximum $g^{(2)}(0)$ of $39.77$. Numerical simulations, benchmarked against experimental measurements, reveal that coherent speckle noise enhances $g^{(2)}(0)$ through mutual superposition with the target pattern, leading to a joint modulation of intensity fluctuations. In summary, by manipulating multiple controllable parameters, we establish a robust strategy for tailoring $g^{(2)}(0)$, paving the way for advanced applications in speckle imaging and optical metrology.

physics.optics↗

arXiv2026

Integrating the advantages of two single-pixel imaging schemes via holographic projection in ghost-imaging systems

Computer-generated hologram (CGH) allows for the on-demand scaling and projection of artificially designed target patterns, while incorporating benefits such as a lensless setup and high-frame-rate operation. In this work, we actively control the projection pattern using CGH and integrate two typical single-pixel imaging (SPI) schemes, thereby implementing a ghost imaging (GI) scheme with flexibly tunable properties. Specifically, various reference signals from computational holography and the corresponding bucket signals are used in the intensity correlation algorithm. Accordingly, those GI results enable the parallel presentation of the outcomes from these two SPI schemes. In the experiment, two types of target patterns, intensity-squared chaotic speckle and artificially designed sparse matrix, are used to perform GI. Those imaging results indicate a significant improvement in ghost image visibility, irrespective of whether the reference signal is the reconstruction or target pattern of computational holography. Furthermore, we realize positive and negative copies of ghost image via holographic projection in which symmetrical mirror target patterns are artificially designed. Thus, by integrating these two SPI schemes, the lensless GI scheme based on CGH not only advances towards the visibility requirements for practical applications but also enables a high-frame-rate projection scheme essential for multi-frame intensity correlation measurements.

physics.optics↗

arXiv2026

SOMA-1M: A Large-Scale SAR-Optical Multi-resolution Alignment Dataset for Multi-Task Remote Sensing

Synthetic Aperture Radar (SAR) and optical imagery provide complementary strengths that constitute the critical foundation for transcending single-modality constraints and facilitating cross-modal collaborative processing and intelligent interpretation. However, existing benchmark datasets often suffer from limitations such as single spatial resolution, insufficient data scale, and low alignment accuracy, making them inadequate for supporting the training and generalization of multi-scale foundation models. To address these challenges, we introduce SOMA-1M (SAR-Optical Multi-resolution Alignment), a pixel-level precisely aligned dataset containing over 1.3 million pairs of georeferenced images with a specification of 512 x 512 pixels. This dataset integrates imagery from Sentinel-1, PIESAT-1, Capella Space, and Google Earth, achieving global multi-scale coverage from 0.5 m to 10 m. It encompasses 12 typical land cover categories, effectively ensuring scene diversity and complexity. To address multimodal projection deformation and massive data registration, we designed a rigorous coarse-to-fine image matching framework ensuring pixel-level alignment. Based on this dataset, we established comprehensive evaluation benchmarks for four hierarchical vision tasks, including image matching, image fusion, SAR-assisted cloud removal, and cross-modal translation, involving over 30 mainstream algorithms. Experimental results demonstrate that supervised training on SOMA-1M significantly enhances performance across all tasks. Notably, multimodal remote sensing image (MRSI) matching performance achieves current state-of-the-art (SOTA) levels. SOMA-1M serves as a foundational resource for robust multimodal algorithms and remote sensing foundation models. The dataset will be released publicly at: https://github.com/PeihaoWu/SOMA-1M.

cs.CV↗

arXiv2025

MapGlue: Multimodal Remote Sensing Image Matching

Multimodal remote sensing image (MRSI) matching is pivotal for cross-modal fusion, localization, and object detection, but it faces severe challenges due to geometric, radiometric, and viewpoint discrepancies across imaging modalities. Existing unimodal datasets lack scale and diversity, limiting deep learning solutions. This paper proposes MapGlue, a universal MRSI matching framework, and MapData, a large-scale multimodal dataset addressing these gaps. Our contributions are twofold. MapData, a globally diverse dataset spanning 233 sampling points, offers original images (7,000x5,000 to 20,000x15,000 pixels). After rigorous cleaning, it provides 121,781 aligned electronic map-visible image pairs (512x512 pixels) with hybrid manual-automated ground truth, addressing the scarcity of scalable multimodal benchmarks. MapGlue integrates semantic context with a dual graph-guided mechanism to extract cross-modal invariant features. This structure enables global-to-local interaction, enhancing descriptor robustness against modality-specific distortions. Extensive evaluations on MapData and five public datasets demonstrate MapGlue's superiority in matching accuracy under complex conditions, outperforming state-of-the-art methods. Notably, MapGlue generalizes effectively to unseen modalities without retraining, highlighting its adaptability. This work addresses longstanding challenges in MRSI matching by combining scalable dataset construction with a robust, semantics-driven framework. Furthermore, MapGlue shows strong generalization capabilities on other modality matching tasks for which it was not specifically trained. The dataset and code are available at https://github.com/PeihaoWu/MapGlue.

cs.CV↗

arXiv2025

Multi-Resolution SAR and Optical Remote Sensing Image Registration Methods: A Review, Datasets, and Future Perspectives

Synthetic Aperture Radar (SAR) and optical image registration is essential for remote sensing data fusion, with applications in military reconnaissance, environmental monitoring, and disaster management. However, challenges arise from differences in imaging mechanisms, geometric distortions, and radiometric properties between SAR and optical images. As image resolution increases, fine SAR textures become more significant, leading to alignment issues and 3D spatial discrepancies. Two major gaps exist: the lack of a publicly available multi-resolution, multi-scene registration dataset and the absence of systematic analysis of current methods. To address this, the MultiResSAR dataset was created, containing over 10k pairs of multi-source, multi-resolution, and multi-scene SAR and optical images. Sixteen state-of-the-art algorithms were tested. Results show no algorithm achieves 100% success, and performance decreases as resolution increases, with most failing on sub-meter data. XoFTR performs best among deep learning methods (40.58%), while RIFT performs best among traditional methods (66.51%). Future research should focus on noise suppression, 3D geometric fusion, cross-view transformation modeling, and deep learning optimization for robust registration of high-resolution SAR and optical images. The dataset is available at https://github.com/betterlll/Multi-Resolution-SAR-dataset-.

cs.CV↗

arXiv2024

ZIKQ: An innovative centile chart method for utilizing natural history data in rare disease clinical development

Utilizing natural history data as external control plays an important role in the clinical development of rare diseases, since placebo groups in double-blind randomization trials may not be available due to ethical reasons and low disease prevalence. This article proposed an innovative approach for utilizing natural history data to support rare disease clinical development by constructing reference centile charts. Due to the deterioration nature of certain rare diseases, the distributions of clinical endpoints can be age-dependent and have an absorbing state of zero, which can result in censored natural history data. Existing methods of reference centile charts can not be directly used in the censored natural history data. Therefore, we propose a new calibrated zero-inflated kernel quantile (ZIKQ) estimation to construct reference centile charts from censored natural history data. Using the application to Duchenne Muscular Dystrophy drug development, we demonstrate that the reference centile charts using the ZIKQ method can be implemented to evaluate treatment efficacy and facilitate a more targeted patient enrollment in rare disease clinical development.

stat.ME↗

arXiv2015

Regression based principal component analysis for sparse functional data with applications to screening growth paths

Growth charts are widely used in pediatric care for assessing childhood body size measurements (e.g., height or weight). The existing growth charts screen one body size at a single given age. However, when a child has multiple measures over time and exhibits a growth path, how to assess those measures jointly in a rigorous and quantitative way remains largely undeveloped in the literature. In this paper, we develop a new method to construct growth charts for growth paths. A new estimation algorithm using alternating regressions is developed to obtain principal component representations of growth paths (sparse functional data). The new algorithm does not rely on strong distribution assumptions and is computationally robust and easily incorporates subject level covariates, such as parental information. Simulation studies are conducted to investigate the performance of our proposed method, including comparisons to existing methods. When the proposed method is applied to monitor the puberty growth among a group of Finnish teens, it yields interesting insights.

stat.AP↗