Image information is restricted by the dynamic range of the detector, which can be addressed using multi-exposure image fusion (MEF). The conventional MEF approach employed in ptychography is often inadequate under conditions of low signal-to-noise ratio (SNR) or variations in illumination intensity. To address this, we developed a Bayesian approach for MEF based on a modified Poisson noise model that considers the background and saturation. Our method outperforms conventional MEF under challenging experimental conditions, as demonstrated by the synthetic and experimental data. Furthermore, this method is versatile and applicable to any imaging scheme requiring high dynamic range (HDR).

翻译：图像信息受探测器动态范围限制，这一问题可通过多曝光图像融合（MEF）解决。叠层成像中采用的传统MEF方法在低信噪比（SNR）或照明强度变化条件下往往表现不足。为此，我们开发了一种基于修正泊松噪声模型（考虑背景和饱和效应）的贝叶斯MEF方法。实验证明，该方法在苛刻实验条件下优于传统MEF，合成数据与实际数据的验证结果均支持这一结论。此外，本方法具有通用性，适用于任何需要高动态范围（HDR）的成像方案。