Stain normalization is a methodological approach within digital image analysis, primarily applied to microscopy images such as those in histopathology, focused on mitigating color variations. It encompasses techniques designed to transform the color profiles of stained images to a consistent standard or reference, thereby reducing inconsistencies arising from variations in staining protocols, scanner calibration, or tissue properties across different samples or laboratories. This concept investigates computational methods, ranging from traditional image processing utilizing color space transformations (e.g., to Optical Density) and mathematical mapping to deep learning-based approaches, for achieving color standardization. Its significance lies in improving the reliability and comparability of images for quantitative analysis, feature extraction, and subsequent tasks like machine learning model training, by ensuring that learned features are robust to color artifacts and enhancing the generalizability of automated systems.