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Restoration of motion-blurred numeral image using a complex-amplitude diffractive processor.

Haodong Zhu, Ruiqi Yin, Tie Hu

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We developed a complex-amplitude diffractive processor using diffractive deep neural networks (D2NNs) to restore motion-blurred numeral images. This advanced processor significantly improves image quality and offers potential for diverse applications.

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Area of Science:

Optics and Photonics
Artificial Intelligence
Image Processing

Background:

Motion blur significantly degrades image quality in numeral recognition.
Diffractive deep neural networks (D2NNs) offer a promising approach for optical information processing.

Purpose of the Study:

To propose and evaluate a complex-amplitude diffractive processor for motion blur removal in numeral images.
To compare the performance of complex-amplitude D2NNs against phase-only and amplitude-only counterparts.

Main Methods:

Development of a complex-amplitude diffractive processor based on D2NNs.
Precise control of optical field propagation for image restoration.
Comparative analysis of different diffractive processor configurations (phase-only, amplitude-only, complex-amplitude).

Main Results:

The complex-amplitude diffractive processor effectively removes motion blur and restores numeral images.
Complex-amplitude networks significantly enhance processor performance and improve peak signal-to-noise ratio (PSNR).
Reduced network layers and alleviated alignment difficulties were observed with complex-amplitude networks.

Conclusions:

Complex-amplitude D2NNs offer superior performance for motion blur removal compared to other configurations.
The proposed processor demonstrates fast processing speed and low power consumption.
Potential applications include road monitoring, sports photography, satellite imaging, and medical diagnostics.