Benchmarking the Gerchberg-Saxton Algorithm
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingSimilar Papers 제목 키워드 기반
Learned residual Gerchberg-Saxton network for computer generated holography
Computer generated holography (CGH) aims to generate phase plates that create an intensity pattern at a certain distance behind the holography plate when illuminated. Since only the intensity and not the phase of the wav…
Optimization of phase retrieval in the Fresnel domain by the modified Gerchberg-Saxton algorithm
The modified Gerchberg-Saxton algorithm (MGSA) is one of the standard methods for phase retrieval. In this work we apply the MGSA in the paraxial domain. For three given physical parameters - i.e. wavelength, propagation…
RetrievalSimultaneous Pre-compensation for Bandwidth Limitation and Fiber Dispersion in Cost-Sensitive IM/DD Transmission Systems
We propose a pre-compensation scheme for bandwidth limitation and fiber dispersion (pre-BL-EDC) based on the modified Gerchberg-Saxton (GS) algorithm. Experimental results demonstrate 1.0/1.0/2.0 dB gains compared to mod…
Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework
Computer-generated holography (CGH) enables applications in holographic augmented reality (AR), 3D displays, systems neuroscience, and optical trapping. The fundamental challenge in CGH is solving the inverse problem of …
BenchmarkingLearning TheoryModel SelectionRetrieval+1A Closer Look at Reference Learning for Fourier Phase Retrieval
Reconstructing images from their Fourier magnitude measurements is a problem that often arises in different research areas. This process is also referred to as phase retrieval. In this work, we consider a modified versio…
Retrieval