Calculating Information Entropy and Mutual Information of Two Images

This document demonstrates how to calculate the information entropy and mutual information between two images using MATLAB. Information entropy serves as a metric to quantify the amount of information contained in an image, while mutual information measures the statistical dependency and correlation between two images. By computing these metrics, we gain valuable insights into image characteristics and their interrelationships. This approach is particularly useful in image processing and analysis, providing essential data for further research and applications.

The implementation typically involves converting images to grayscale, calculating probability distributions of pixel intensities, and applying entropy formulas. Key MATLAB functions used include entropy() for single-image entropy calculation and custom implementations for joint entropy and mutual information computation. The mutual information algorithm follows the standard approach: MI(X,Y) = H(X) + H(Y) - H(X,Y), where H(X) and H(Y) represent individual image entropies, and H(X,Y) denotes their joint entropy.

This methodology supports various image analysis tasks such as image registration, feature matching, and similarity assessment, making it fundamental for computer vision and medical imaging applications.