Histogram Equalization
Histogram equalization is a digital image processing technique used to enhance the contrast of an image by redistributing its intensity values. It works by transforming the histogram of an image so that the cumulative distribution function becomes linear, resulting in a more uniform spread of pixel intensities. This method is widely applied in computer vision, medical imaging, and photography to improve visual quality and feature detection.
Developers should learn histogram equalization when working on image enhancement tasks, such as in medical imaging to highlight subtle details in X-rays or MRIs, or in computer vision applications like object recognition where better contrast can improve algorithm performance. It's particularly useful in low-contrast images or when preprocessing data for machine learning models that rely on visual features, as it standardizes brightness and makes patterns more discernible.