arXiv subjects

Alexander Doronin

Publications and source records attributed to Alexander Doronin.

6 recordsLinked to original sources

arXiv2026

Topological Polarimetry: Polarization Skyrmions and Vortices from Tissue Scattering

The Stokes-Mueller description of tissue polarimetry is conventionally interpreted through local observables such as birefringence, depolarization, and helicity preservation. We show that tissue structure generates topology in the polarization field itself. Polarization-resolved measurements of unstained human breast tissue reveal polarization vortices and, where the tissue architecture provides coupled azimuthal and radial variation, Neel-type polarization skyrmions. These structures are quantified by the vortex winding number and skyrmion charge, integer-valued topological invariants that vanish for homogeneous media and therefore provide zero background readouts of tissue heterogeneity. Malignant ductal carcinoma exhibits non-trivial polarization topology, whereas adjacent healthy tissue remains topologically trivial. The results establish topological invariants of the polarization field as physically interpretable observables of tissue organization and motivate topological polarimetry as a field-based approach to tissue characterization.

physics.optics↗

arXiv2026

SSD-GS: Scattering and Shadow Decomposition for Relightable 3D Gaussian Splatting

We present SSD-GS, a physically-based relighting framework built upon 3D Gaussian Splatting (3DGS) that achieves high-quality reconstruction and photorealistic relighting under novel lighting conditions. In physically-based relighting, accurately modeling light-material interactions is essential for faithful appearance reproduction. However, existing 3DGS-based relighting methods adopt coarse shading decompositions, either modeling only diffuse and specular reflections or relying on neural networks to approximate shadows and scattering. This leads to limited fidelity and poor physical interpretability, particularly for anisotropic metals and translucent materials. To address these limitations, SSD-GS decomposes reflectance into four components: diffuse, specular, shadow, and subsurface scattering. We introduce a learnable dipole-based scattering module for subsurface transport, an occlusion-aware shadow formulation that integrates visibility estimates with a refinement network, and an enhanced specular component with an anisotropic Fresnel-based model. Through progressive integration of all components during training, SSD-GS effectively disentangles lighting and material properties, even for unseen illumination conditions, as demonstrated on the challenging OLAT dataset. Experiments demonstrate superior quantitative and perceptual relighting quality compared to prior methods and pave the way for downstream tasks, including controllable light source editing and interactive scene relighting. The source code is available at: https://github.com/irisfreesiri/SSD-GS.

cs.CV↗

arXiv2026

MSGS: Multispectral 3D Gaussian Splatting

We present a multispectral extension to 3D Gaussian Splatting (3DGS) for wavelength-aware view synthesis. Each Gaussian is augmented with spectral radiance, represented via per-band spherical harmonics, and optimized under a dual-loss supervision scheme combining RGB and multispectral signals. To improve rendering fidelity, we perform spectral-to-RGB conversion at the pixel level, allowing richer spectral cues to be retained during optimization. Our method is evaluated on both public and self-captured real-world datasets, demonstrating consistent improvements over the RGB-only 3DGS baseline in terms of image quality and spectral consistency. Notably, it excels in challenging scenes involving translucent materials and anisotropic reflections. The proposed approach maintains the compactness and real-time efficiency of 3DGS while laying the foundation for future integration with physically based shading models.

cs.CV↗

arXiv2025

Decoding Orbital Angular Momentum in Turbid Tissue-like Scattering Medium via Fourier-Domain Deep Learning

Structured light beams carrying orbital angular momentum (OAM), such as Laguerre-Gaussian modes, are promising tools for high-capacity optical communications and advanced biomedical imaging. However, multiple scattering in turbid media distorts their phase and amplitude, complicating the retrieval of topological charge. We introduce VortexNet, a deep learning architecture that integrates an Angular Fourier Transform to explicitly extract rotational symmetries of OAM beams from experimentally acquired intensity and interference patterns. By transforming spatial information into the angular frequency domain, VortexNet isolates azimuthal features that persist despite scattering, enabling accurate topological charge classification even in complex optical environments. The results reveal that OAM-specific angular correlations can survive multiple scattering and be decoded through angular-domain learning. This establishes a new paradigm for structured-light analysis in complex medium, where deep learning enables the recovery of topological information beyond the reach of classical optics, paving the way for resilient photonic systems in communication, sensing, and imaging.

physics.optics↗

arXiv2024

Machine Learning Model for Complete Reconstruction of Diagnostic Polarimetric Images from partial Mueller polarimetry data

The translation of imaging Mueller polarimetry to clinical practice is often hindered by large footprint and relatively slow acquisition speed of the existing instruments. Using polarization-sensitive camera as a detector may reduce instrument dimensions and allow data streaming at video rate. However, only the first three rows of a complete 4x4 Mueller matrix can be measured. To overcome this hurdle we developed a machine learning approach using sequential neural network algorithm for the reconstruction of missing elements of a Mueller matrix from the measured elements of the first three rows. The algorithm was trained and tested on the dataset of polarimetric images of various excised human tissues (uterine cervix, colon, skin, brain) acquired with two different imaging Mueller polarimeters operating in either reflection (wide-field imaging system) or transmission (microscope) configurations at different wavelengths of 550 nm and 385 nm, respectively. The reconstruction performance was evaluated using various error metrics, all of which confirmed low error values. The execution time of the trained neural network algorithm was about 300 microseconds for a single image pixel. It suggests that a machine learning approach with parallel processing of all image pixels combined with the partial Mueller polarimeter operating at video rate can effectively substitute for the complete Mueller polarimeter and produce accurate maps of depolarization, linear retardance and orientation of the optical axis of biological tissues, which can be used for medical diagnosis in clinical settings.

physics.optics↗

arXiv2024

Insights into Polycrystalline Microstructure of Blood Films with 3D Mueller Matrix Imaging Approach

We introduce a 3D Mueller Matrix (MM) image reconstruction technique using digital holographic approach for the layer-by-layer profiling thin films with polycrystalline structures, like dehydrated blood smears. The proposed method effectively extracts optical anisotropy parameters for a detailed quantitative analysis. The investigation revealed the method sensitivity to subtle changes in optical anisotropy properties resulting from alterations in the quaternary and tertiary structures of blood proteins, leading to disturbances in crystallization structures at the macro level at the very early stage of a disease. Spatial distributions of linear and circular birefringence and dichroism are analyzed in partially depolarizing polycrystalline blood films obtained from healthy tissues and cancerous prostate tissues at various stages of adenocarcinoma. Changes in the values of the 1st to 4th order statistical moments, characterizing the distributions of optical anisotropy in different phase sections of the smear volumes, are observed and quantified. Comparative analysis of optical anisotropy distributions from healthy patients highlighted the 3rd and 4th order statistical moments for linear and circular birefringence and dichroism as the most promising for diagnostic purposes. We achieved an excellent accuracy (>90%) for early cancer diagnosis and differentiation of its stages, demonstrating the techniques significant potential for rapid and accurate definitive cancer diagnosis compared to existing screening approaches.

physics.med-ph↗