Histogram Equalization Processing

A key method mentioned in this article is histogram equalization processing. Histogram equalization is an image processing technique that enhances image contrast and brightness by redistributing pixel intensity values. This algorithm works by transforming the image's histogram to achieve a more uniform distribution of pixel intensities across the entire dynamic range, thereby improving visual quality. In implementation, this typically involves calculating the cumulative distribution function of the image histogram and applying a transformation mapping to equalize the intensity values. From a programming perspective, histogram equalization can be implemented using functions like cv2.equalizeHist() in OpenCV, or through custom algorithms that compute probability distribution functions and cumulative sums. The technique finds applications in various image processing domains including image enhancement, segmentation, and feature extraction. Histogram equalization serves as a simple yet effective approach to improve image quality, making images more suitable for analysis and interpretation by enhancing overall contrast and revealing subtle details that might otherwise be overlooked.