Histogram Equalization Using MMBEBHE Image Processing Algorithm

Histogram equalization is a classic image enhancement technique used to improve image contrast. In traditional histogram equalization methods, global adjustments may cause certain areas to become overexposed or too dark, potentially losing detail. MMBEBHE (Modified Minimum Mean Brightness Error Bi-Histogram Equalization) is an improved algorithm designed to overcome these limitations.

The core principles of MMBEBHE involve: Image Segmentation: Dividing the input image's histogram into two parts (highlight and low-light regions), typically based on mean brightness or optimal segmentation threshold. In code implementation, this involves calculating the cumulative distribution function and finding the optimal split point that minimizes brightness error. Independent Equalization: Performing separate equalization processing on the two sub-histograms to avoid brightness shift issues caused by global adjustments. This is implemented by applying distinct transformation functions to each histogram partition. Brightness Preservation: Optimizing the segmentation point to minimize the average brightness difference before and after processing, ensuring more natural visual results. The algorithm achieves this through iterative threshold optimization or mathematical minimization techniques.

Compared to traditional methods, MMBEBHE better preserves the overall brightness of images while enhancing local contrast, making it suitable for applications requiring high brightness consistency such as medical imaging and surveillance systems. Enhanced versions of this algorithm may incorporate adaptive thresholding or weighted optimization techniques to further improve image quality. Code implementations typically involve histogram calculation, threshold detection, and piecewise transformation functions using libraries like OpenCV or MATLAB's image processing toolbox.