MATLAB Implementation of Image Processing Functions

This article presents self-developed image processing functions that encompass histogram plotting, histogram specification, and histogram equalization capabilities. The algorithmic implementations are based on the methodologies described in "Image Engineering (Volume 1) - Image Processing (2nd Edition)" by Zhang Yujin, published by Tsinghua University Press. These MATLAB functions implement core image processing techniques through systematic computational approaches: - The histogram plotting function calculates pixel intensity distribution across 256 bins using imhist() or custom accumulation algorithms, providing visual analysis of image brightness characteristics. - Histogram specification employs probability density function mapping with interp1() for interpolation, transforming original histograms to match target distributions through cumulative distribution function matching. - Histogram equalization utilizes transform functions like histeq() to redistribute pixel intensities, enhancing contrast by flattening and stretching the histogram dynamically. The practical applications of these functions extend across multiple domains: - Histogram plotting enables quantitative analysis of illumination distribution in digital images - Histogram specification facilitates precise contrast adjustment for medical imaging and photographic enhancement - Histogram equalization improves feature visibility in computer vision systems and surveillance applications These implementations serve both academic research purposes and practical software development, providing optimized solutions for image processing toolboxes and application-specific enhancements. The referenced textbook offers comprehensive theoretical foundations and practical implementation guidelines for these algorithms, making it an essential resource for mastering digital image processing techniques.