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：图像信息受限于探测器的动态范围，这一问题可通过多曝光图像融合（MEF）加以解决。在叠层成像中，传统的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。为解决此问题，我们基于考虑背景和饱和效应的修正泊松噪声模型，开发了一种贝叶斯MEF方法。合成数据与实验数据表明，该方法在具有挑战性的实验条件下优于传统MEF。此外，该方法具有通用性，可适用于任何需要高动态范围（HDR）的成像方案。