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Research direction

Computational Imaging

Physics-aware inverse methods, compressive and coded sensing, and learning-based reconstruction for next-generation imaging systems.

Graphical abstract for Computational Imaging

Overview

The group designs imaging systems end-to-end, jointly optimizing the optical coding (apertures, illumination patterns, and filter arrays) together with the computational reconstruction that follows. This co-design lets a physically simple sensor capture far more information than a conventional camera of the same size and cost.

Recent work spans compressive X-ray computed tomography and Compton backscattering, snapshot hyperspectral and spectral-video imaging, and deep unfolding networks that embed the physics of the forward model directly into the reconstruction.

Compressive sensing Coded apertures Hyperspectral imaging X-ray CT Deep unfolding Snapshot imaging
Publications

Papers in this direction

2026
Focal Spot Mitigation with Dynamic Sampling Conditions for High-Resolution X-ray CT via Diffusion Priors
C. M. Restrepo-Galeano, G. R. Arce
Optics Express
2025
Degradation-Estimated Hybrid Unfolding Transformer Network for Efficient Hyperspectral Image Reconstruction
Z. Fang, X. Ma, G. R. Arce
Optics & Laser Technology
2025
A Deep Estimation-Enhancement Unfolding Framework for Hyperspectral Image Reconstruction
Z. Fang, X. Ma, G. R. Arce
Infrared Physics & Technology
2025
Illumination Pattern Optimization in Compressive X-ray Compton Backscattering Imaging
A. K. Alrushud, E. Salazar, G. Arce
Optics Express
2025
Sudoku Multispectral Filter Arrays for Spectral Snapshot Cameras
A. Aguirre, A. Alrushud, G. Arce, D. Lau
Optics Continuum
2023
High-Resolution LED-Based Snapshot Compressive Spectral Video Imaging with Deep Neural Networks
X. Ma, X. Yuan, G. R. Arce
IEEE Transactions on Computational Imaging