Image Grayscale Stretching and Histogram Equalization Implementation

In the given task, we process an image through several key operations. First, we generate the image's histogram to analyze the distribution of pixel values, which can be implemented using functions like numpy.histogram() in Python or imhist() in MATLAB. This provides crucial statistical insights into the image's intensity characteristics. Next, we implement a linear transformation function for grayscale stretching, which can be coded using the formula: output_pixel = a * input_pixel + b, where parameters a and b control contrast enhancement and brightness adjustment respectively. This operation expands the dynamic range of pixel values to improve image contrast and reveal finer details. Finally, we perform histogram equalization using algorithms that redistribute pixel intensities to achieve a more uniform brightness distribution. This can be implemented through cumulative distribution function (CDF) mapping, where each pixel value is transformed based on the image's CDF. The equalization process enhances image clarity and makes features more distinguishable for visual analysis. These operations collectively provide comprehensive image enhancement capabilities, allowing for better understanding and processing of digital images through histogram analysis, contrast enhancement, and brightness normalization techniques.