CALL FOR PAPER
Conference Tracks
Track 1: Mathematical Theory and Methods for Computational Imaging
This track focuses on mathematical modeling, inverse problem solving, and numerical algorithms in computational imaging systems, covering the full chain from physical sensing to image reconstruction.
• Forward model description and approximation for imaging systems (PSF, OTF, blur kernel modeling)
• Inverse problems and regularization theory in computational imaging (Tikhonov regularization, total variation, sparsity constraints)
• Mathematical algorithms for phase retrieval and incoherent imaging (Gerchberg–Saxton, Fienup algorithm)
• Mathematical frameworks for coded aperture imaging and compressive sensing (measurement matrix design, sparse reconstruction theory)
• Statistical models for single pixel imaging and ghost imaging (intensity correlation, fluctuation analysis)
• Mathematical reconstruction algorithms for non line of sight imaging (transient imaging models, photon path integrals)
• Deep learning assisted inverse problem solving for imaging (physics informed neural networks, unrolled networks)
• Mathematical theory of resolution limits and super resolution reconstruction in computational imaging
Track 2: Wave Optics and Electromagnetic Field Modeling
This track addresses mathematical modeling and numerical simulation methods for light propagation, scattering, diffraction, and electromagnetic fields in micro/nanostructures.
• Numerical solutions of Maxwell’s equations (FDTD, FEM, FDFD, BEM)
• Efficient algorithms for the Helmholtz equation and wave propagation
• Radiative transfer equation and Monte Carlo simulation of light transport
• Light transport modeling in scattering media (atmospheric scattering, biological tissue scattering, fog/haze)
• Statistical modeling of coherent and partially coherent optical fields (cross spectral density, degree of coherence)
• Rigorous coupled wave analysis (RCWA) and Fourier modal method
• Optical response modeling of subwavelength structures and plasmonics
• Multi scale optical simulation methods (coupled modeling from nano to macro scales)
Track 3: Mathematical Tools for Optical System Design and Optimization
This track targets mathematical methods for automated design, tolerance analysis, and performance optimization of optical systems (lenses, mirrors, freeforms, metasurfaces, etc.).
• Mathematical formulations of ray tracing and aberration theory (Seidel aberrations, Zernike polynomials)
• Global optimization algorithms for automated optical design (genetic algorithms, particle swarm optimization, simulated annealing)
• Mathematical representations for freeform optical design (NURBS, radial basis functions, XY polynomials)
• Tolerance analysis and sensitivity modeling for optical systems (Monte Carlo simulation, statistical tolerance analysis)
• Deep learning based inverse design of lenses and metasurfaces
• Mathematical evaluation methods for diffraction limits and Strehl ratio
• Dynamic modeling of thermal and vibrational effects on optical systems
• Model based calibration and compensation strategies for optics
Track 4: Digital Holography and Light Field Modeling
This track focuses on mathematical models and numerical algorithms for digital holographic reconstruction, light field rendering, and 3D displays.
• Numerical algorithms for digital holographic reconstruction (angular spectrum method, Fresnel approximation, convolution method)
• Optimization problems and algorithms for computer generated holograms (CGH)
• Mathematical models for phase imaging and quantitative phase measurement
• Geometric models for light field sampling, parameterization, and rendering (light field cameras, microlens arrays)
• Mathematical models for view synthesis and depth in 3D displays
• Real time computational methods for holographic displays and augmented reality (AR)
• Mathematical frameworks for light field compression and sparse representation
• Noise suppression and phase unwrapping algorithms in digital holography
Track 5: Mathematical Models for Optical Metrology and Measurement
This track addresses signal modeling, data inversion, and uncertainty quantification in optical precision measurement.
• Phase extraction algorithms in interferometry (phase shifting algorithms, Fourier transform method, wavelet transform)
• Numerical models for white light interferometry and coherence scanning interferometry
• Inverse solving and parameter retrieval in ellipsometry and scatterometry
• Geometric and algebraic models for 3D optical topography reconstruction
• Inversion, calibration, and deconvolution algorithms for spectral measurements
• Bayesian inference and confidence interval estimation for optical measurement data
• Monte Carlo simulation for tolerance and uncertainty analysis in optical systems
• Mathematical frameworks for model verification and validation (V&V)
Track 6: Computational Modeling of Photonic Devices and Materials
This track targets multi physics modeling of novel photonic structures and devices, including photonic crystals, metamaterials, metasurfaces, and optical waveguides.
• Eigenvalue problem solutions for photonic crystals and photonic bandgap structures
• Effective medium models and S‑parameter retrieval for metamaterials and metasurfaces
• Optimization algorithms and global search for multilayer optical thin‑film design
• Mode solving and propagation modeling for optical waveguides and fiber devices
• Rate‑equation and multi‑mode modeling for semiconductor lasers and optical amplifiers
• Mathematical descriptions of nonlinear optical processes (Kerr effect, harmonic generation, four‑wave mixing)
• Optical response modeling of 2D materials (graphene, TMDs)
• Multi‑physics simulation of opto‑thermo‑electric coupling and PDE systems
Track 7: Data‑Driven and Machine Learning‑Enhanced Optical Modeling
This track explores innovative applications of machine learning, deep learning, and data‑driven methods in optical modeling and design.
• Physics‑informed neural networks (PINNs) for optical simulation and inverse problems
• Gaussian processes and Bayesian optimization for optical system design
• Deep generative models (GANs, VAEs) for optical image enhancement and reconstruction
• Neural network acceleration for Maxwell’s equations and wave equation solvers
• Machine learning for data inversion and parameter prediction in optical metrology
• Data‑driven performance prediction and optimization of optical devices
• Transfer learning and meta‑learning for optical modeling
• Explainable AI (XAI) methods in optical design and imaging
Track 8: Optical Signal Processing and Information‑Theoretic Models
This track addresses mathematical models for optical signal and image processing, information‑theoretic analysis, and system performance evaluation.
• Signal models and image reconstruction for optical coherence tomography (OCT)
• Data fusion and tensor decomposition for spectral/hyperspectral imaging
• Mathematical frameworks for optical encryption and secure optical communications
• Information‑theoretic analysis of resolution limits and information capacity in optical imaging
• Denoising, deblurring, and super‑resolution reconstruction algorithms for optical images
• Mathematical methods for multi‑modal optical information fusion
• Wavefront sensing and real‑time control modeling for adaptive optics
• Mathematical models for real‑time optical signal processing and visual computing
Submit Your Paper
Authors are invited to submit original, unpublished research aligned with the conference themes. Accepted papers will be published in conference proceedings and submitted to EI and Scopus for indexing.
Where & How to Submit
All submissions must be uploaded exclusively via Easychair.