首页|极大似然噪声估计的高动态范围叠层衍射成像术

极大似然噪声估计的高动态范围叠层衍射成像术

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衍射场作为叠层衍射成像技术(ptychography)的重要约束,其信息的丰富度和准确性将直接影响重构质量.提出一种基于极大似然噪声估计的高动态范围(ML-HDR)叠层衍射成像方法,即在探测器线性响应假设下,构建复合高斯噪声模型,根据极大似然估计求解最优权重函数,由多张低动态范围衍射场合成高信噪比衍射场.对比了单次曝光、传统HDR和ML-HDR三种方法的重构质量.仿真和实验结果表明:相比单次曝光,ML-HDR能将动态范围拓宽8位,重构分辨率提升至2.83倍;相比传统HDR,ML-HDR能提高重构图像的均匀性和对比度,且无需额外标定硬件参数.
High-Dynamic-Range Ptychography Using Maximum Likelihood Noise Estimation
As crucial constraints of ptychography,the richness and accuracy of diffraction patterns directly affect the quality of reconstruction images.This paper proposes a high-dynamic-range ptychography using maximum likelihood noise estimation(ML-HDR).Herein,assuming the linear response of the detector,a compound Gaussian noise model is established;the weight function is optimized according to the ML estimation;and a high signal-to-noise ratio diffraction pattern is further synthesized from multiple low dynamic range diffraction patterns.The reconstruction quality of single exposure,conventional HDR,and ML-HDR is compared.The simulation and experiment results show that ML-HDR can widen the dynamic range by 8 bits and enhance the reconstruction resolution by 2.83 times compared with the single exposure.Moreover,compared with conventional HDR,ML-HDR can enhance the contrast and uniformity of the reconstruction image in the absence of additional hardware parameters.

computational imagingptychographyhigh dynamic rangephase retrievalmaximum likelihood estimation

李文杰、谷洪刚、刘力、钟磊、周玉、刘世元

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华中科技大学智能制造装备与技术全国重点实验室,湖北 武汉 430074

光谷实验室,湖北 武汉 430074

计算成像 叠层衍射成像术 高动态范围 相位恢复 极大似然估计

国家自然科学基金湖北省重点研发计划

521305042021BAA013

2024

激光与光电子学进展
中国科学院上海光学精密机械研究所

激光与光电子学进展

CSTPCD北大核心
影响因子:1.153
ISSN:1006-4125
年,卷(期):2024.61(8)
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