Histogram of Image Gray-Level Distribution

This MATLAB source code demonstrates how to display histograms of image gray-level distributions. The implementation utilizes MATLAB's built-in image processing functions to analyze pixel intensity distribution across different gray levels. The program is highly valuable for image analysis as it helps understand the gray-level distribution characteristics of images. Through the histogram visualization, users can observe the pixel count for each gray-level intensity, enabling comprehensive analysis of image brightness and contrast properties. The implementation typically involves functions like imread() to load images, rgb2gray() for color-to-gray conversion (if needed), and hist() or imhist() specifically designed for image histogram computation. Beyond histogram analysis, MATLAB provides extensive powerful functionalities for image processing, signal analysis, and data manipulation. Key advantages include comprehensive toolboxes like the Image Processing Toolbox, which offers advanced functions for filtering, transformation, and morphological operations. Learning and utilizing MATLAB remains highly beneficial for technical computing, despite its specialized user base, as it serves as an essential tool for research and engineering applications requiring sophisticated numerical computation and visualization capabilities. The code implementation typically follows this workflow: image loading → optional color space conversion → histogram calculation using binning techniques → visualization with proper axis labeling and normalization options. This approach ensures accurate representation of the image's tonal distribution while maintaining computational efficiency.