Novel approach based on stochastic gradient descent for controlling the reconstructed phase randomness of computer-generated holograms. (July 2023)

• Record Type: Journal Article
• Authors: Shen, Chenhang; Zheng, Yuhang; Le, Zichun
• Is Part Of: Optics and lasers in engineering. Volume 166(2023)
• Publication Date: 2023-07
• Subjects: Reconstructed holograms -- Reconstructed phase randomness -- Loss function -- Pre-calculation -- Depth of field; ASM angular spectrum method -- CGH computer-generated hologram -- DPAC double phase-amplitude coding -- MSE mean squared error -- PPOH patterned-phase-only hologram -- RPs random phases -- SGD stochastic gradient descent -- SLM spatial light modulator -- SSIM structural similarity index measure -- PSNR peak signal-to-noise ratio; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2023.107593
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: CGH method combines pre-calculation and standard deviation-based loss. Hologram is adjusted to visual stimuli perceived by the human eye. Adding random phases by SGD algorithm. Depth of field is adjusted by controlling reconstructed phase randomness. Abstract: Depth of field, as one of the essential adjustment cues, affects the user experience and restricts the application of holographic displays. However, it is often underappreciated in modern holographic displays. In this study, we propose a novel computer-generated hologram (CGH) method that uses a pre-calculation procedure and a standard deviation-based loss function to generate realistic depth-of-field effects. The depth-of-field size is adjusted by controlling the randomness of the reconstructed phase. The ability of the proposed algorithm to control the reconstructed phase randomness was confirmed. Finally, a holographic display prototype was built to demonstrate the proposed method experimentally. The experimental results were consistent with the simulation results. Our findings may open up a new perspective on the application of existing stochastic gradient descent (SGD) algorithms to traditional methods for image quality optimization by adding random phases.