Image Enhancement in Machine Learning: the Ultimate Guide

Image enhancement improves an image's visual quality by adjusting its features like brightness, contrast, sharpness, color, etc. The main goal of image enhancement is to make the image more visually appealing and easier to interpret - both for humans and machine learning models. There are two main methods for performing image enhancement: Spatial Domain Methods and Frequency Domain Methods. Image augmentation is a technique in computer vision to supplement the dataset with artificial variations of existing images, while image enhancement encompasses a wide range of techniques aimed at improving the quality and visual appeal of an image. Some examples of image enhancement include histogram equalization, gamma correction, contrast stretching, sharpening, noise reduction, and image dehazing.