Image Contrast Enhancement

In image processing, various methods can be employed. One effective approach involves utilizing grayscale histograms for image analysis. A grayscale histogram represents a statistical chart of an image that displays the frequency of occurrence for each gray level within the image. By analyzing the grayscale histogram, crucial information about the image can be obtained, such as brightness distribution and contrast characteristics. Through histogram equalization techniques, we can redistribute pixel intensities to enhance image contrast. A typical implementation involves calculating cumulative distribution functions and mapping original intensity values to new values. Common functions like cv2.equalizeHist() in OpenCV or histeq() in MATLAB automate this process by stretching the intensity range to cover the full available spectrum. By leveraging grayscale histogram analysis, developers can gain deeper insights into image properties and perform more effective image enhancement operations.