Histogram equalization is a powerful technique used to enhance the contrast of images by adjusting the intensity distribution. In grayscale images, each pixel is represented by a single value denoting its intensity, typically ranging from 0 to 255. This value corresponds to the brightness of the pixel, with 0 representing black and 255 representing white.
This project aims to implement histogram equalization for grayscale images using parallel processing techniques, specifically leveraging the Message Passing Interface (MPI) for distributed computing. By utilizing MPI along with C++, we can distribute the workload across multiple processing units, such as CPU cores or compute nodes, to accelerate the histogram equalization process.
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C++: The project is developed using the C++ programming language, providing high performance and flexibility for implementing complex algorithms.
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MPI (Message Passing Interface): MPI is utilized for distributed computing, enabling communication and coordination among multiple computing nodes or processes for parallel execution.