Image Pre-processing

Image pre-processing applies techniques that improve image quality, standardize input data, or highlight important visual features. Common operations include resizing, cropping, normalization, noise reduction, contrast enhancement, color space conversion, and image augmentation. These steps help ensure that images are in a consistent format for model training and inference.

Raw images often vary in size, lighting, quality, and orientation, which can negatively affect model performance. Image pre-processing helps reduce these inconsistencies, improve data quality, and enable machine learning models to learn more effectively from visual data.