Histogram Equalization and Brightness Adjustment Techniques

Among the fundamental programs in image processing, we can explore various methods for histogram equalization and brightness adjustment. Histogram equalization is a common image enhancement technique that redistributes image pixel values to achieve more balanced contrast and clearer image details. This algorithm typically involves calculating the cumulative distribution function of image pixel intensities and mapping them to a uniform distribution, which can be implemented using functions like MATLAB's histeq() or OpenCV's equalizeHist(). Brightness adjustment allows modification of image luminance based on specific characteristics and requirements, making images brighter or darker through techniques such as simple pixel value scaling or gamma correction. Key implementation approaches include adding/subtracting constant values to pixel intensities or applying nonlinear transformations using power-law functions. By studying these fundamental image processing programs, we can better understand and apply the principles and techniques of digital image manipulation, particularly through practical implementation using matrix operations and intensity transformation functions common in programming libraries.