Benchmarking the Gerchberg-Saxton Algorithm

TL;DR

This paper benchmarks the Gerchberg-Saxton algorithm for digital holography, analyzing its convergence, performance, and efficiency trade-offs across various applications and SLM types.

Contribution

It provides a comprehensive analysis of the Gerchberg-Saxton algorithm's trade-offs in convergence speed, accuracy, and efficiency, highlighting factors influencing runtime and convergence.

Findings

Identifies key factors affecting convergence and runtime.

Quantifies trade-offs between accuracy and efficiency.

Provides guidelines for optimizing hologram generation algorithms.

Abstract

Due to the proliferation of spatial light modulators, digital holography is finding wide-spread use in fields from augmented reality to medical imaging to additive manufacturing to lithography to optical tweezing to telecommunications. There are numerous types of SLM available with a multitude of algorithms for generating holograms. Each algorithm has limitations in terms of convergence speed, power efficiency, accuracy and data storage requirement. Here, we consider probably the most common algorithm for computer generated holography - Gerchberg-Saxton - and examine the trade-off in convergent quality, performance and efficiency. In particular, we focus on measuring and understanding the factors that control runtime and convergence.