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

Abstract

In the conventional weighted Gerchberg-Saxton (GS) algorithm, the feedback is used to accelerate the convergence. However, it will lead to the iteration divergence. To solve this issue, an adaptive weighted GS algorithm is proposed in this paper. By replacing the conventional feedback with our designed feedback, the convergence can be ensured in the proposed method. Compared with the traditional GS iteration method, the proposed method improves the peak signal-noise ratio of the reconstructed image with 4.8 dB on average. Moreover, an approximate quadratic phase is proposed to suppress the artifacts in optical reconstruction. Therefore, a high-quality image can be reconstructed without the artifacts in our designed Argument Reality device. Both numerical simulations and optical experiments have validated the effectiveness of the proposed method.

Realistic Defocus Blur for Multiplane Computer-Generated Holography

“…Specifically, when the target depth planes are packed closely in terms of distances (a few millimeters), coherent image blur dominates the naïve approach's solution, leading to noisy reconstructions suffering from visual quality issues, specifically in defocus parts. The recent and common literature that uses optimization [Wu et al 2021] and machine learning methods [Lee et al 2020] widely adopts the naïve approach. Hence, we argue that multiplane image reconstruction is beyond resolving an optimization or a learning problem, and it has to also deal with the problem formulation.…”

Cimmino simultaneously iterative holographic projection

“…Wyrowski and Bryngdahl [29] also suggested using a quadratic phase as a means of improving the overall image quality of the holographic display. More recently, Wu et al, have also looked into adapting Gerchberg-Saxton with this technique, and have seen significant improvements in image quality with a PSNR improvement of 4.8dB on average [30].…”

“…Furthermore, related to the work carried out by Wu et al [30], Chen et al have used Stochastic gradient descent to demonstrate improvements in image quality [31]. Deep learning and neural networks in holography are additional separate methods for improving image quality, however, these are not covered in this work [7,32,33], since the purpose of this work is finding the link between the traditional linear algebraic solving algorithms such as Kaczmarz and Cimmino, with more modern holography display algorithms.…”

Compact Computational Holographic Display

“…Detour phase (Arrizón et al, 1998;Bingxia Wang et al, 2021) and double-phase methods (Hsueh and Sawchuk, 1978;Song et al, 2012;Mendoza-Yero et al, 2014) are typical analytical techniques, which are fast but with compromised qualities, and can be easily applied to arbitrary complex holograms. Optimization techniques such as Gerchberg-Saxton (GS) (Gerchberg, 2002;Sun et al, 2018;Chen et al, 2020;Wu et al, 2021) are time-consuming with improved image quality. However, these are difficult to be applied to arbitrary complex fields (Chakravarthula et al, 2019).…”