Color image information transmission based on elliptic optical vortex array encoding/decoding

Area of Science:

• Optics and Photonics
• Information Technology
• Computer Science

Background:

• High-dimensional data encoding and decoding are crucial for modern communication systems.
• Existing optical communication methods face limitations in transmission efficiency and capacity.
• Elliptic optical vortex (EOV) arrays offer potential for advanced optical data manipulation.

Purpose of the Study:

• To propose and demonstrate a multichannel high-dimensional data encoding/decoding scheme using composite EOV arrays.
• To enhance optical data transmission efficiency by encoding multiple channels onto a single hologram.
• To validate the scheme's feasibility through experimental transmission of a color image.

Main Methods:

• Utilized a 4x4 composite EOV array for high-dimensional data encoding based on rotation angles.
• Employed conjugate symmetric extension Fourier computer-generated holography for controllable reconstruction focus across RGB channels.
• Implemented a deep learning-based convolutional neural network for decoding intensity patterns at the receiver.

Main Results:

• Successfully encoded and transmitted a 128x128-pixel color image using the proposed scheme.
• Demonstrated simultaneous transmission of RGB color image data through a single hologram.
• Confirmed the feasibility and effectiveness of the EOV-based encoding/decoding approach.

Conclusions:

• The proposed composite EOV array-based scheme offers an efficient method for multichannel high-dimensional optical data transmission.
• Deep learning-based decoding provides a robust approach for retrieving transmitted information.
• This technology holds significant potential for future high-capacity optical communication applications.