This study introduces an expert system designed for assessing image quality in diffraction problems, utilizing a linearly polarized beam. As an illustrative example, the tasks addressed when light passes through a double-phase rampconverter are considered. The system employs statistical, empirical, and Bayesian inference methods to develop a comprehensive knowledge base comprising rules and methodologies. These methods are divided into two categories: quantitative metrics and expert evaluation. Quantitative metrics rely on established criteria like signal-to-noise ratio, contrast ratio, and structural similarity index, alongside topological similarity values computed using specific formulas. Expert evaluation involves assessing parameters influencing image quality, prioritizing them for different sets of experimental images, and formulating pertinent questions regarding image quality at the preliminary and final stages. Looking ahead, future developments in expert systems could advance in two pivotal directions. Firstly, machine learning techniques can be utilized to improve the recognition of topological objects within images. Secondly, by broadening the capabilities of expert systems to tackle a wider range of diffraction problems, thereby enhancing their applicability and efficacy.