Computational Optical Imaging: Theories, Algorithms, and Applications

Dear Colleagues,

Computational imaging is increasingly prevalent in autonomous systems and biomedical applications, enabled by advances in sensors and reconstruction algorithms. Raw sensor measurements are often difficult to interpret, creating ill-posed inverse problems and necessitating principled methods to recover meaningful, quantitative information. Augmenting the information content of measured data—through denoising and spatial and temporal resolution enhancement—is also critical. This Special Issue focuses on theories, algorithms, and systems that jointly incorporate sensing and computation to achieve robust imaging under challenging conditions, including sparse or compressed photon regimes, scattering media, long-range and non-line-of-sight scenarios, event-driven neuromorphic sensing, and low-emission constraints in biomedical imaging and spectroscopy. Emphasis is placed on statistical and optimization frameworks, physics-guided models, and data-driven machine learning approaches that improve accuracy, interpretability, and efficiency across diverse applications.

Topics of interest include (but are not limited to) the following:

• Foundational theory for computational imaging and inverse problems;
• Imaging sensors;
• Statistical and optimization methods for robust reconstruction and inference;
• Machine learning for optical imaging;
• Compressive and sparse imaging;
• Single-photon imaging;
• Neuromorphic/event-based sensing and algorithms
• Statistics and optimization algorithms in imaging;
• Computational biomedical imaging and spectroscopic reconstruction.

We look forward to receiving your contributions.

Dr. Zhenya Zang
Dr. Yiwei Chen
Dr. Dong Xiao
Guest Editors

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• computational imaging
• compressive imaging
• single-photon imaging
• biomedical imaging and sensing
• statistics and optimization imaging algorithms
• applied machine learning in imaging
• smart imaging sensors