Abstract: Diffractive imaging technology, widely applied in remote sensing imaging, biological microscopic observation and material structure analysis, is an advanced optical imaging technology that reconstructs object information via deconvolution of observation-derived diffraction images with the point spread function (PSF). However, the actually measured PSF is prone to interference from imaging system optical component defects and detector noise, leading to discrepancies from the true PSF and thus degraded diffractive imaging performance. Therefore, the research on PSF correction technology is of great significance.After a comprehensive review of domestic and international literature, this paper first summarizes and analyzes the causes of PSF degradation, including inherent limitations of imaging components, dynamic interference, and complex scenarios. It then traces the evolution of PSF correction methods, ranging from optical hardware and traditional computational algorithms (such as modulation transfer function (MTF)-based fusion models and PSF modeling) to deep learning and data-driven approaches (including phase mask optimization networks and BGnet). These methods provide solutions for PSF correction in various application scenarios. Finally, the paper introduces the application of PSF correction techniques in fields such as image processing, astronomical and space imaging, and biomedical imaging. In the future, with interdisciplinary integration and advancements in related technologies, PSF correction techniques will offer stronger support for areas like celestial exploration and microscopic structural observation.