MATLAB Image Processing Algorithms: Sharpening, Contrast Adjustment, and Noise Removal

This document includes three fundamental image processing algorithms implemented in MATLAB: image sharpening using the Laplacian operator (which enhances edges by calculating the second derivative of the image), contrast adjustment through histogram manipulation or gamma correction, and noise removal using median filters (replacing each pixel with the median value of its neighborhood to effectively eliminate salt-and-pepper noise). These implementations are particularly helpful for MATLAB beginners and those interested in image processing fundamentals.

For users seeking deeper understanding of image processing techniques, further exploration of advanced algorithms is recommended, including edge detection methods (like Sobel or Canny operators), image segmentation techniques (such as watershed or k-means clustering), and image enhancement approaches. These algorithms help users better comprehend the principles and practical applications of image processing, often implemented using MATLAB's built-in functions like edge(), imsegkmeans(), or histeq().

Beyond MATLAB, other powerful image processing tools and libraries are available, such as OpenCV in Python (offering real-time computer vision capabilities) and OpenGL in C++ (providing high-performance graphics rendering). Familiarity with different tools and libraries expands users' capabilities in the image processing field, allowing them to choose appropriate solutions based on project requirements.

In summary, this document provides basic image processing algorithms suitable for beginners and enthusiasts. However, the image processing domain is extensive, with numerous additional algorithms and tools worth exploring, including frequency-domain processing, morphological operations, and machine learning-based approaches that can be implemented across various programming environments.