MATLAB Implementation of Histogram Matching

Histogram matching, also referred to as histogram specification, is a fundamental image processing technique. This implementation uses MATLAB programming rather than relying on the histeq function from the Image Processing Toolbox (IPT) for specification purposes. Histogram matching serves as a powerful method for adjusting image contrast and brightness characteristics. By aligning an input image's histogram with a target histogram distribution, this technique effectively modifies the overall luminance and contrast properties of images. The implementation typically involves three main computational steps: calculating the cumulative distribution function (CDF) of both source and target images, mapping the source image intensities to match the target distribution, and applying the transformation to the input image. This approach requires manual calculation of probability density functions and careful intensity mapping, providing greater control compared to built-in functions. This method finds extensive applications in various image processing domains including image enhancement, color correction, medical imaging, and photographic post-processing. The custom implementation allows researchers to modify the matching algorithm according to specific requirements and understand the underlying mathematical operations.