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Fast Near-Field Frequency-Diverse Computational Imaging Based on End-to-End Deep-Learning Network.

Zhenhua Wu^1,2,3, Fafa Zhao¹, Man Zhang⁴

¹Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China.

Sensors (Basel, Switzerland)

|December 23, 2022

Summary

This summary is machine-generated.

This study introduces a deep convolutional neural network for computational imaging, improving metasurface antenna performance. The new method enhances image reconstruction from limited data, reducing computational load and widening operational bands.

Keywords:

computational imaging deep convolutional neural network metasurface antennas near field imaging

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Area of Science:

Electromagnetics and Applied Physics
Computational Imaging
Metasurface Antennas

Background:

Metasurface antennas offer advanced wave sculpting but face trade-offs between bandwidth and radiation pattern characteristics.
Existing computational imaging methods, like matched filters and sparsity-driven algorithms, struggle with low sampling ratios and high computational complexity.
A need exists for advanced reconstruction techniques to overcome limitations in current metasurface antenna imaging.

Purpose of the Study:

To develop a novel computational imaging approach using deep convolutional neural networks (CNNs) for metasurface antennas.
To address the challenges of low scene sampling ratios and high computational complexity in current imaging techniques.
To improve the reconstruction accuracy and efficiency for various targets, from point-size objects to complex scenes.

Main Methods:

Integration of deep convolutional neural network (CNN) principles with computational imaging.
Development of a trained reconstruction network capable of handling high correlation of measurement modes and low scene sampling ratios.
Comparison against traditional matched filter and compressed sensing reconstruction techniques.

Main Results:

The proposed CNN-based method effectively reconstructs both point-size objects and complex targets with high accuracy and speed.
Demonstrated ability to handle high correlation of measurement modes and low scene sampling ratios, outperforming existing methods.
Significant mitigation of computational burden and requirements for large operation frequency bands.

Conclusions:

The deep convolutional neural network approach offers a powerful solution for computational imaging with metasurface antennas.
This method overcomes key limitations of traditional algorithms, enabling more efficient and accurate image reconstruction.
The findings pave the way for enhanced performance and broader applicability of metasurface antenna systems.