存在泊松-高斯噪声统计的叠层衍射成像中的最大似然估计
Maximum-likelihood estimation in ptychography in the presence of Poisson-Gaussian noise statistics
AI总结：
针对低信噪比下混合泊松-高斯噪声损害图像质量的问题，提出显式包含该噪声特性的最大似然损失函数，在梯度优化中考虑相机读出噪声，实验与数值结果均优于传统方法。
摘要：
光学测量通常表现出混合的泊松-高斯噪声统计特性，这会损害图像质量，尤其是在低信噪比（SNR）条件下。当仅假设泊松噪声统计时，计算成像在这种情况下表现不佳。针对这一挑战，我们定义了一个显式包含这种混合噪声特性的损失函数。通过使用最大似然估计，我们设计了一种实用方法，以在基于梯度的叠层衍射成像优化中考虑相机读出噪声。基于实验和数值数据的结果表明，该方法优于传统方法，通过一种直接的方法调整，能够在具有挑战性的噪声条件下增强图像重建质量。
英文摘要：
Optical measurements often exhibit mixed Poisson-Gaussian noise statistics, which hampers image quality, particularly under low signal-to-noise ratio (SNR) conditions. Computational imaging falls short in such situations when solely Poissonian noise statistics are assumed. In response to this challenge, we define a loss function that explicitly incorporates this mixed noise nature. By using maximum-likelihood estimation, we devise a practical method to account for camera readout noise in gradient-based ptychography optimization. Our results, based on both experimental and numerical data, demonstrate that this approach outperforms the conventional one, enabling enhanced image reconstruction quality under challenging noise conditions through a straightforward methodological adjustment.
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