Fourier ptychographic microscopy (FPM) is an innovative computational microscopy approach that enables high-throughput imaging with high resolution, wide field of view, and quantitative phase imaging (QPI) by simultaneously capturing bright-field and dark-field images. However, effectively utilizing dark-field intensity images, including both normally exposed and overexposed data, which contain valuable high-angle illumination information, remains a complex challenge. Successfully extracting and applying this information could significantly enhance phase reconstruction, benefiting processes such as virtual staining and QPI imaging. To address this, we introduce a multi-exposure image fusion (MEIF) framework that optimizes dark-field information by incorporating it into the FPM preprocessing workflow. MEIF increases the data available for reconstruction without requiring changes to the optical setup. We evaluate the framework using both feature-domain and traditional FPM, demonstrating that it achieves substantial improvements in intensity resolution and phase information for biological samples that exceed the performance of conventional high dynamic range (HDR) methods. This image preprocessing-based information-maximization strategy fully leverages existing datasets and offers promising potential to drive advancements in fields such as microscopy, remote sensing, and crystallography.