Several Improved Algorithms for Histogram Equalization

Histogram equalization is an image enhancement technique that improves image contrast by expanding the dynamic range of pixel intensities. In practical implementations, several improved algorithms can be employed to further enhance the performance of standard histogram equalization. The BBHE (Brightness Preserving Bi-Histogram Equalization) algorithm addresses the issue of over-enhancement commonly found in standard histogram equalization by separating the histogram based on mean brightness values and independently processing sub-images. The DSIHE (Dualistic Sub-Image Histogram Equalization) algorithm effectively reduces noise artifacts by dividing the histogram using median values and maintaining brightness conservation. The MMBEBHE (Minimum Mean Brightness Error Bi-Histogram Equalization) algorithm further improves equalization quality by incorporating neighborhood pixel information and minimizing brightness distortion through optimal threshold selection. When implementing these algorithms in code, key functions typically include histogram calculation, threshold determination, cumulative distribution function computation, and mapping function generation. Therefore, based on different application scenarios such as medical imaging, surveillance systems, or photographic enhancement, appropriate improved algorithms can be selected to achieve optimal image enhancement results while maintaining natural appearance and minimizing artifacts.