U-Net Driven High-Resolution Complex Field Information Prediction in Single-Shot Four-Step Phase-Shifted Digital Holography Using Polarization Camera
We present a novel high-resolution complex field extraction technique utilizing U-Net-based architecture to effectively overcome the inherent resolution limitations of polarization cameras with micro-polarized arrays. Our method extracts high-resolution complex field information, achieving a resolut...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Photonics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-6732/11/12/1172 |
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| Summary: | We present a novel high-resolution complex field extraction technique utilizing U-Net-based architecture to effectively overcome the inherent resolution limitations of polarization cameras with micro-polarized arrays. Our method extracts high-resolution complex field information, achieving a resolution comparable to that of the original polarization camera. Utilizing the parallel phase-shifting digital holography technique, we extracted high-resolution complex field information from four high-resolution phase-shifted interference patterns predicted by our network directly at the hologram plane. Extracting the object’s complex field directly at the hologram plane rather than the object’s plane, our method eliminates the dependency on numerical propagation during dataset acquisition, enabling reconstruction of objects at various depths without DC and conjugate noise. By training the network with real-valued interference patterns and using only a single pair of low- and high-resolution input and ground truth interference patterns, we simplify computational complexity and improve efficiency. Our simulations demonstrate the network’s robustness to variations in random phase distributions and transverse shifts in the input patterns. The effectiveness of the proposed method is demonstrated through numerical simulations, showing an average improvement of over 4 dB in peak-signal-to-noise ratio and 25% in intensity normalized cross-correlation metrics for object reconstruction quality. |
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| ISSN: | 2304-6732 |