Image preprocessing fundamentals

Before extracting patterns or building predictive models from image data, it is essential to ensure that every image is properly prepared for analysis. Image preprocessing forms the foundation of any computer vision workflow, as it provides a standardized input for our analysis process.

Images from various sources often differ in size, orientation, lighting, and color balance. These differences can distort measurements, confuse feature detectors, or introduce bias in downstream models. Preprocessing techniques such as resizing, denoising, thresholding, and color correction help remove these variations, ensuring that subsequent analyses reflect true structures rather than artifacts.

This section will cover key parts of image preprocessing. You will learn how to load and display images in Python, handle pixel data and color models, reduce noise while preserving important edges, and apply binary and geometric transformations.

Each subsection...