Digital Hologram Construction and Reconstruction of Non-Near-Field Holography in Computational Holography

In computational holography, Non-Near-Field Holography is a digital holographic method that differs from traditional near-field holography. It overcomes the distance limitations inherent in conventional holography during recording and reconstruction processes while enabling more flexible handling of complex wavefront information.

Digital Hologram Construction The core of Non-Near-Field Hologram construction lies in digital wavefront simulation and encoding. The process begins with numerical computation to simulate the diffraction wavefront of target objects in far-field or intermediate-field regions. The simulated wavefront data is then converted into phase or amplitude distributions for hologram generation. Common encoding methods include phase modulation or amplitude modulation techniques, often implemented using Fourier transforms or angular spectrum methods for diffraction field calculations. Key advantages include precise parameter control through algorithmic optimization (e.g., using FFT algorithms for efficient field propagation) and adaptability to various optical system requirements through programmable modulation patterns.

Hologram Reconstruction Reconstruction can be achieved through optical devices or numerical methods. Optical reconstruction employs Spatial Light Modulators (SLMs) to load digital holograms and generate reconstructed wavefronts, where device calibration algorithms ensure accurate phase modulation. Numerical reconstruction performs inverse computations to extract original wavefront information from holograms, typically involving back-propagation algorithms with phase compensation. Special attention must be paid to diffraction effects and phase compensation in far-field conditions, often addressed through iterative algorithms like Gerchberg-Saxton for fidelity enhancement.

Applications and Challenges Non-Near-Field Holography finds applications in 3D display systems, optical encryption, and complex wavefront manipulation. However, it faces challenges including high computational complexity (requiring GPU acceleration for large-scale simulations) and stringent hardware requirements (such as SLM resolution limitations). Current research focuses on algorithm optimization through machine learning approaches and hardware performance improvements via high-precision modulation techniques.