分歧(语言学)
趋同(经济学)
计算机科学
算法
全息术
相位恢复
图像质量
相(物质)
噪音(视频)
全息显示器
迭代重建
二次方程
自适应光学
光学
图像(数学)
数学
计算机视觉
物理
傅里叶变换
量子力学
经济增长
语言学
数学分析
哲学
经济
几何学
作者
Yang Wu,Jun Wang,Chun Chen,Chanjuan Liu,Fengming Jin,Ni Chen
出处
期刊:Optics Express
[Optica Publishing Group]
日期:2020-12-20
卷期号:29 (2): 1412-1412
被引量:100
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI