Global Histogram Equalization and Local Histogram Equalization: Code Implementation and Applications

This article presents two distinct histogram equalization techniques: global histogram equalization and local histogram equalization. These methods serve different image enhancement scenarios and can significantly improve visual quality when applied appropriately. Global histogram equalization operates by analyzing the pixel value distribution across the entire image. The algorithm calculates the cumulative distribution function (CDF) of all pixel intensities and maps them to create a uniform distribution, thereby enhancing overall contrast. This method is particularly effective for images with poor global contrast but may over-enhance noisy regions. Local histogram equalization (often implemented as adaptive histogram equalization) processes the image in small, overlapping tiles or neighborhoods. For each pixel location, the algorithm computes the histogram within a specified local window (typically 8x8 or 16x16 pixels) and applies equalization to that region. This approach preserves and enhances local details while maintaining natural appearance, making it suitable for images with varying contrast across different regions. Both implementations include key functions for histogram calculation, CDF computation, and mapping transformation. The global method uses a single transformation function for the entire image, while the local method dynamically generates multiple transformation functions based on local neighborhoods. Users can select the appropriate technique based on their specific image characteristics: global equalization for uniform contrast improvement, or local equalization for detailed texture enhancement without amplifying noise in homogeneous regions. By utilizing these complementary approaches, image processing practitioners can effectively address diverse contrast enhancement requirements in various applications including medical imaging, surveillance systems, and photographic enhancement.