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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

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Advancing Multi-Dimensional Vision: AI-Driven Imaging Using Unique Photodetectors with Integrated Surface

Ahasan Ahamed¹, Weiwei Wang¹, Amita Rawat¹

¹Electrical and Computer Engineering Department, University of California-Davis, California, 95616, USA.

Proceedings of Spie--The International Society for Optical Engineering

|March 2, 2026

Summary

This summary is machine-generated.

This study introduces compact photodetector assemblies with nanostructures and AI-driven imaging. This breakthrough enhances multi-dimensional data capture, enabling smaller, more sensitive, and cost-effective imaging systems.

Keywords:

Computational Imaging Nanostructures Photodetector Spectroscopy

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Area of Science:

Optics and Photonics
Nanotechnology
Computational Imaging

Background:

Current imaging systems face limitations in scale, sensitivity, and cost.
Capturing multi-dimensional data (spectral, temporal, spatial) is crucial for advanced imaging.
Individual ultrafast detectors show varied responses to illumination, posing a challenge for data consistency.

Purpose of the Study:

To demonstrate a novel compact photodetector assembly with enhanced imaging capabilities.
To showcase the integration of artificial intelligence (AI)-driven computational imaging for data optimization.
To highlight the potential for reduced system size, increased sensitivity, and lower costs in imaging modalities.

Main Methods:

Engineering compact photodetector assemblies with innovative surface nanostructures.
Developing individual ultrafast detectors with tailored response characteristics.
Integrating artificial intelligence (AI) for computational imaging and multi-dimensional data optimization.

Main Results:

Demonstrated significant enhancement in imaging modalities through compact photodetector assemblies.
Achieved capture of multi-dimensional data, including spectral, temporal, and spatial information.
Showcased potential for reduced physical scale, enhanced system sensitivity, and cost reduction.

Conclusions:

The developed compact photodetector assembly offers a significant advancement in imaging technology.
AI-driven computational imaging effectively optimizes multi-dimensional data from these novel detectors.
Potential applications span molecular fluorescence, chem-biological imaging, LiDAR, and focal plane arrays.