Analysis and Implementation of Grayscale Histogram Specification Algorithm

In this document, I will provide a comprehensive analysis of the implementation of the grayscale histogram specification algorithm. Grayscale histogram specification is an image processing technique that enhances image quality by adjusting the pixel value distribution. Through histogram specification, we can improve image contrast and make details more distinct. Additionally, histogram equalization can be employed to enhance the visual effect of images. These techniques are crucial for image processing and enhancement applications. In this article, I will delve into the principles and applications of these techniques, providing detailed algorithm analysis and implementation methods. The implementation typically involves calculating cumulative distribution functions, mapping pixel values using transformation functions, and applying interpolation methods for continuous tone adjustment. Key functions include histogram calculation, probability distribution computation, and pixel value mapping algorithms. This analysis will help readers better understand and apply the grayscale histogram specification algorithm in practical image processing projects.