Computational ghost imaging encryption with a pattern compression from 3D to 0D.

Area of Science:

• Optics and Photonics
• Information Security
• Computational Imaging

Background:

• Computational ghost imaging (GI) shows promise for optical encryption.
• A major challenge is managing the large number of keys (patterns) required.
• Existing methods face limitations due to pattern quantity.

Purpose of the Study:

• To propose and evaluate novel pattern compression methods for computational GI.
• To reduce the number of patterns needed for optical encryption.
• To enhance the security and practicality of GI-based encryption systems.

Main Methods:

• Developed pattern compression techniques for computational GI.
• Replaced large sets of patterns with single images (2D), sequences (1D), or irrational number fractions (0D).
• Validated methods through simulations and experimental tests, analyzing error tolerance.

Main Results:

• Successfully compressed thousands of patterns into significantly smaller data formats.
• Demonstrated the feasibility of proposed compression methods in both simulated and real-world scenarios.
• Quantified the error tolerance of the compression techniques for encryption applications.

Conclusions:

• The proposed pattern compression methods effectively reduce pattern requirements for computational GI.
• These methods enhance encryption security and overcome practical obstacles in optical encryption.
• The work advances the application of computational GI in secure optical communication and data protection.