Adaptive weighted Gerchberg-Saxton algorithm for generation of phase-only hologram with artifacts suppression

Area of Science:

• Computational imaging
• Optical reconstruction algorithms
• Digital holography

Background:

• Conventional Gerchberg-Saxton (GS) algorithms utilize feedback to accelerate convergence.
• However, this feedback can lead to iteration divergence, compromising reconstruction quality.
• Artifacts in optical reconstruction further degrade the final image.

Purpose of the Study:

• To propose an adaptive weighted GS algorithm that ensures convergence and suppresses artifacts.
• To enhance the peak signal-to-noise ratio (PSNR) in image reconstruction.
• To achieve high-quality optical reconstruction for augmented reality devices.

Main Methods:

• Developed a novel adaptive feedback mechanism to replace conventional feedback in the GS algorithm.
• Introduced an approximate quadratic phase to mitigate reconstruction artifacts.
• Validated the proposed method through numerical simulations and optical experiments.

Main Results:

• The adaptive weighted GS algorithm demonstrated ensured convergence, unlike the conventional method.
• Achieved an average improvement of 4.8 dB in peak signal-to-noise ratio (PSNR).
• Successfully reconstructed high-quality images free from artifacts in an augmented reality device.

Conclusions:

• The proposed adaptive weighted GS algorithm effectively overcomes the divergence issue of conventional methods.
• The integration of approximate quadratic phase significantly suppresses artifacts in optical reconstruction.
• The validated method enables high-fidelity image reconstruction for advanced optical systems.