像素
相位恢复
光学
计算机科学
全息术
稳健性(进化)
正规化(语言学)
图像分辨率
迭代重建
图像质量
探测器
鬼影成像
物理
计算机视觉
人工智能
图像(数学)
生物化学
化学
量子力学
傅里叶变换
基因
作者
Xuyang Chang,Liheng Bian,Yunhui Gao,Liangcai Cao,Jun Zhang
出处
期刊:Optics Letters
[The Optical Society]
日期:2022-05-17
卷期号:47 (11): 2658-2658
被引量:11
摘要
In order to increase signal-to-noise ratio in optical imaging, most detectors sacrifice resolution to increase pixel size in a confined area, which impedes further development of high throughput holographic imaging. Although the pixel super-resolution technique (PSR) enables resolution enhancement, it suffers from the trade-off between reconstruction quality and super-resolution ratio. In this work, we report a high-fidelity PSR phase retrieval method with plug-and-play optimization, termed PNP-PSR. It decomposes PSR reconstruction into independent sub-problems based on generalized alternating projection framework. An alternating projection operator and an enhancing neural network are employed to tackle the measurement fidelity and statistical prior regularization, respectively. PNP-PSR incorporates the advantages of individual operators, achieving both high efficiency and noise robustness. Extensive experiments show that PNP-PSR outperforms the existing techniques in both resolution enhancement and noise suppression.
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