arXiv subjects

Fanglin Bao

Publications and source records attributed to Fanglin Bao.

12 recordsLinked to original sources

arXiv2026

T$^2$exture: Sparsely Perturbed Thermal-to-Texture Imaging

Thermal imaging remains effective under adverse illumination, yet passive long-wave infrared (LWIR) measurements often lack fine texture. Existing thermal texture imaging approaches commonly rely on spectral sensing or registered auxiliary modalities, incurring substantial data throughput or vulnerability to cross-modal degradation. We introduce T$^2$exture, a sparsely perturbed thermal texture imaging framework that aims to reconstruct temporally dense thermal texture sequences from densely sampled passive frames and a few actively perturbed keyframes. We define thermal texture as the residual between a source-on observation and its corresponding source-off passive state. Under sparse LWIR illumination and rapid quasi-steady paired acquisition, this residual attenuates the passive-emission background and approximates a source-induced reflected response, exposing localized material- and geometry-dependent texture. T$^2$exture reconstructs a dense sequence of this source-conditioned response through two stages. Stage 1 estimates the unobserved source-off passive state at each active instant from neighboring passive frames to obtain reliable differential texture anchors. Stage 2 combines sparse anchors with passive structural context near each target time to reconstruct the dense sequence. On the simulated benchmark, T$^2$exture adds only 0.20M parameters to AMT-L while improving PSNR by 6.66 dB. Extensive evaluations on simulated and real acquisitions further show clearer texture recovery and stronger structural preservation than representative VFI baselines. These results establish T$^2$exture as a practical framework for thermal texture imaging under sparse active acquisition.

cs.CV↗

arXiv2026

TeX-1500: A Paired Real-World LWIR Hyperspectral Dataset and Benchmark for Temperature-Emissivity-Texture Decomposition

Temperature-emissivity-texture (TeX) decomposition seeks to recover object heat state, material spectral response, and visible-like geometric texture from long-wave infrared hyperspectral imaging (LWIR HSI). Existing TeX pipelines are mainly scene-specific inverse solvers, and the lack of paired LWIR HSI-TeX supervision has limited learning-based decomposition. To address this gap, we introduce TeX-1500, a large-scale paired LWIR HSI-TeX dataset and benchmark for supervised HSI-to-TeX decomposition. TeX-1500 contains 1,522 calibrated real-scene pairs from DARPA Invisible Headlights (DARPA IH) pushbroom imagery and our FTIR acquisitions, covering five locations, four seasons, diverse acquisition times, heterogeneous wavelength layouts, and two sensor families. Each sample stores a calibrated valid-band radiance cube, calibrated wavelength positions, and aligned temperature, emissivity, and texture supervision constructed through a consistent restoration and TeX-construction protocol. We further provide TeX-UNet, a simple wavelength-aware baseline that maps calibrated HSI bands and wavelength positions to TeX fields. Experiments on the held-out DARPA IH pushbroom scenes and zero-/few-shot transfer to FTIR scenes show that TeX-1500 provides usable paired supervision and a measurable benchmark for data-driven physical-property-centered thermal perception.

cs.CV↗

arXiv2026

Quantum-inspired Topographic Stereovision

We revisit the conventional triangulation in distant stereovision, when shape rather than distance is the relevant observable. We show through the information-regret analysis that the optimal measurements for absolute distance and relative topography are unexpectedly different and incompatible, exposing an observable-measurement mismatch. To resolve this, we introduce stereo regularization to address stereo anisotropies that violate prevailing emitter-number conservation. Accordingly, we propose a topographic interferometer, which exploits cross-detector correlations to probe topography without measuring the distance profile. Our Fizeau-imaging interferometer turns parallax paths into Mach-Zehnder arms and employs a central path as the local oscillator for balanced homodyne detection, saturating the quantum Fisher information with improved topographic error scaling. This enables topographic stereovision of thermal sources beyond the Rayleigh limit, with feasible experimental demonstrations within existing techniques for remote sensing and astronomy.

quant-ph↗

arXiv2026

HADAR-Based Thermal Infrared Hyperspectral Image Restoration

Thermal-infrared (TIR) hyperspectral imagery (HSI) provides critical scene information for various applications. However, its practical utility is severely limited by unique sensor degradations beyond the capabilities of existing restoration methods, which are ignorant of underlying thermal physics. Here, we propose HAIR (HADAR-based Image Restoration) as a physics-driven framework for ground-based TIR-HSI restoration. HAIR utilizes the HADAR rendering equation (HRE) and combines it with the atmospheric downwelling radiative transfer equation (RTE) to model TIR-HSI using temperature, emissivity, and texture (TeX) physical triplets. This physical model leads to a TeX decompose-synthesize strategy that guarantees physical consistency and spatio-spectral noise resilience, in stark contrast to existing approaches. Moreover, our framework uses a forward-modeled atmospheric downwelling reference, along with spectral smoothness of emissivity and blackbody radiation, to enable spectral calibration and generation that would otherwise be elusive. Our extensive experiments on the outdoor DARPA Invisible Headlights dataset and in-lab FTIR measurements show that HAIR consistently outperforms state-of-the-art methods across denoising, inpainting, spectral calibration, and spectral super-resolution, establishing a benchmark in objective accuracy and visual quality.

cs.CV↗

arXiv2026

Universal computational thermal imaging overcoming the ghosting effect

Thermal imaging is crucial for night vision but fundamentally hampered by the ghosting effect, a loss of detailed texture in cluttered photon streams. While conventional ghosting mitigation has relied on data post-processing, the recent breakthrough in heat-assisted detection and ranging (HADAR) opens a promising frontier for hyperspectral computational thermal imaging that produces night vision with day-like visibility. However, universal anti-ghosting imaging remains elusive, as state-of-the-art HADAR applies only to limited scenes with uniform materials, whereas material non-uniformity is ubiquitous in the real world. Here, we propose a universal computational thermal imaging framework, TAG (thermal anti-ghosting), to address material non-uniformity and overcome ghosting for high-fidelity night vision. TAG takes hyperspectral photon streams for nonparametric texture recovery, enabling our experimental demonstration of unprecedented expression recovery in thus-far-elusive ghostly human faces -- the archetypal, long-recognized ghosting phenomenon. Strikingly, TAG not only universally outperforms HADAR across various scenes, but also reveals the influence of material non-uniformity, shedding light on HADAR's effectiveness boundary. We extensively test facial texture and expression recovery across day and night, and demonstrate, for the first time, thermal 3D topological alignment and mood detection. This work establishes a universal foundation for high-fidelity computational night vision, with potential applications in autonomous navigation, reconnaissance, healthcare, and wildlife monitoring.

cs.CV↗

arXiv2024

Spinning Metasurface Stack for Spectro-polarimetric Thermal Imaging

Spectro-polarimetric imaging in the long-wave infrared (LWIR) region plays a crucial role in applications from night vision and machine perception to trace gas sensing and thermography. However, the current generation of spectro-polarimetric LWIR imagers suffer from limitations in size, spectral resolution and field of view (FOV). While meta-optics-based strategies for spectro-polarimetric imaging have been explored in the visible spectrum, their potential for thermal imaging remains largely unexplored. In this work, we introduce a novel approach for spectro-polarimetric decomposition by combining large-area stacked meta-optical devices with advanced computational imaging algorithms. The co-design of a stack of spinning dispersive metasurfaces along with compressed sensing and dictionary learning algorithms allows simultaneous spectral and polarimetric resolution without the need for bulky filter wheels or interferometers. Our spinning-metasurface-based spectro polarimetric stack is compact (< 10 x 10 x 10 cm), robust, and offers a wide field of view (20.5°). We show that the spectral resolving power of our system substantially enhances performance in machine learning tasks such as material classification, a challenge for conventional panchromatic thermal cameras. Our approach represents a significant advance in the field of thermal imaging for a wide range of applications including heat-assisted detection and ranging (HADAR).

physics.optics↗

arXiv2023

Why are thermal images blurry

The resolution of optical imaging is limited by diffraction as well as detector noise. However, thermal imaging exhibits an additional unique phenomenon of ghosting which results in blurry and low-texture images. Here, we provide a detailed view of thermal physics-driven texture and explain why it vanishes in thermal images capturing heat radiation. We show that spectral resolution in thermal imagery can help recover this texture, and we provide algorithms to recover texture close to the ground truth. Using a simulator for complex 3D scenes, we discuss the interplay of geometric textures and non-uniform temperatures which is common in real-world thermal imaging. We demonstrate the failure of traditional thermal imaging to recover ground truth in multiple scenarios while our thermal perception approach successfully recovers geometric textures. Finally, we put forth an experimentally feasible infrared Bayer-filter approach to achieve thermal perception in pitch darkness as vivid as optical imagery in broad daylight.

physics.optics↗

arXiv2022

Nonequilibrium Hanbury-Brown-Twiss experiment: Theory and application to binary stars

Intensity-interferometry based on Hanbury-Brown and Twiss's seminal experiment for determining the radius of the star Sirius formed the basis for developing the quantum theory of light. To date, the principle of this experiment is used in various forms across different fields of quantum optics, imaging and astronomy. Though, the technique is powerful, it has not been generalized for objects at different temperatures. Here, we address this problem using a generating functional formalism by employing the P-function representation of quantum-thermal light. Specifically, we investigate the photon coincidences of a system of two extended objects at different temperature using this theoretical framework. We show two unique aspects in the second-order quantum coherence function - interference oscillations and a long-baseline asymptotic value that depends on the observation frequency, temperatures and size of both objects. We apply our approach to the case of binary stars and discuss the advantages of measuring these two features in an experiment. In addition to the estimation of the radii of each star and the distance between them, we also show that the present approach is suitable for the estimation of temperatures as well. To this end, we apply it to the practical case of binary stars Luhman 16 and Spica $α$ Vir. We find that for currently available telescopes, an experimental demonstration is feasible in the near term. Our work contributes to the fundamental understanding of intensity interferometry of quantum-thermal light and can be used as a tool for studying two-body thermal emitters - from binary stars to extended objects.

quant-ph↗

arXiv2021

Quantum Entropic Causal Inference

The class of problems in causal inference which seeks to isolate causal correlations solely from observational data even without interventions has come to the forefront of machine learning, neuroscience and social sciences. As new large scale quantum systems go online, it opens interesting questions of whether a quantum framework exists on isolating causal correlations without any interventions on a quantum system. We put forth a theoretical framework for merging quantum information science and causal inference by exploiting entropic principles. At the root of our approach is the proposition that the true causal direction minimizes the entropy of exogenous variables in a non-local hidden variable theory. The proposed framework uses a quantum causal structural equation model to build the connection between two fields: entropic causal inference and the quantum marginal problem. First, inspired by the definition of geometric quantum discord, we fill the gap between classical and quantum conditional density matrices to define quantum causal models. Subsequently, using a greedy approach, we develop a scalable algorithm for quantum entropic causal inference unifying classical and quantum causality in a principled way. We apply our proposed algorithm to an experimentally relevant scenario of identifying the subsystem impacted by noise starting from an entangled state. This successful inference on a synthetic quantum dataset can have practical applications in identifying originators of malicious activity on future multi-node quantum networks as well as quantum error correction. As quantum datasets and systems grow in complexity, our framework can play a foundational role in bringing observational causal inference from the classical to the quantum domain.

cs.ET↗

arXiv2021

Quantum-accelerated imaging of N stars

Imaging point sources with low angular separation near or below the Rayleigh criterion is important in astronomy, e.g., in the search for habitable exoplanets near stars. However, the measurement time required to resolve stars in the sub-Rayleigh region via traditional direct imaging is usually prohibitive. Here we propose quantum-accelerated imaging (QAI) to significantly reduce the measurement time using an information-theoretic approach. QAI achieves quantum acceleration by adaptively learning optimal measurements from data to maximize Fisher information per detected photon. Our approach can be implemented experimentally by linear-projection instruments followed by a single-photon detector array. We estimate the position, brightness and the number of unknown stars $10\sim100$ times faster than direct imaging with the same aperture. QAI is scalable to large number of incoherent point sources and can find widespread applicability beyond astronomy to high-speed imaging, fluorescence microscopy and efficient optical read-out of qubits.

physics.optics↗

arXiv2015

Comment on "Cutoff dependence of the Casimir force within an inhomogeneous medium"

Horsley and Simpson [Phys. Rev. A 88, 013833 (2013)] recently claimed that the inhomogeneous Casimir pressure in a piston model is cutoff dependent, and diverges when the cutoff parameter is removed (ξ->0). We demonstrate that, there is a miscalculation in their derivation, and our correction results in a cutoff independent Casimir pressure, based on first-order perturbation theory. We give the general expressions of first-order perturbative inhomogeneous Casimir energy which make it convenient to analyze the divergence problem or to yield the Casimir force. The Casimir pressure contribution of parallel waves (with wave vector parallel to the Casimir plates) together with the non-commutativity of limit and summation operators, are discussed and found to be useful for understanding the inhomogeneous divergence declared in another paper [Phys. Rev. A 87, 043806 (2013)]. We should emphasize that we cannot yet give an exact expression of inhomogeneous Casimir pressure beyond first-order perturbation, which is worth future investigation.

physics.optics↗

arXiv2015

First-order correction to the Casimir force within an inhomogeneous medium

For the Casimir piston filled with an inhomogeneous medium, the Casimir energy is regularized and expressed with cylinder kernel coefficients by using the first-order perturbation theory. When the refraction index of the medium is smoothly inhomogeneous (i.e., derivatives of all orders exist), logarithmically cutoff-dependent term in Casimir energy is found. We show that in the piston model this term vanishes in the force and thus the Casimir force is always cutoff-independent, but this term will remain in the force in the half-space model and must be removed by additional regularization. We investigate the inhomogeneity of an exponentially decaying profile, and give the first-order corrections to both free Casimir energy and Casimir force. The present method can be extended to other inhomogeneous profiles. Our results should be useful for future relevant calculations and experimental studies.

physics.optics↗