Three Histogram-Based Algorithms: Implementation and Analysis

This implementation covers three fundamental histogram-based image processing algorithms. Each algorithm is housed in its own dedicated directory for modular organization. The Histogram Equalization technique enhances global contrast by uniformly distributing pixel intensity values across the histogram. The Histogram Specification method allows custom contrast enhancement by matching the image histogram to a target distribution. The Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm improves local contrast while limiting amplification of noise through tile-based processing and clip limiting. To execute the algorithms, simply run the test files provided in each directory. The test scripts include sample image loading, parameter initialization, and result visualization functions. For optimal results, users can modify parameters such as clip limits for CLAHE or reference histograms for specification in the configuration sections. Beyond basic implementation, we can further analyze each algorithm's performance characteristics, computational complexity, advantages/disadvantages, and specific applications in digital image processing. The code structure supports easy extension for performance benchmarking and comparative studies across different image types.