🎨 Histogram Equalization to Improve Image Contrast

🚀 Comparison between sequential and parallel implementations

This project explores Histogram Equalization to enhance image contrast, comparing a sequential version with an optimized parallel version using CUDA.

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🛠️ Features

• ⬆️ Image contrast enhancement through Histogram Equalization.
• ⏱️ Performance comparison between sequential and parallel versions.
• 📁 Support for .jpg, .png, and .bmp image files.
• 💾 Results are saved in a dedicated folder.

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🖥️ Languages and Libraries Used

✅ C++
✅ OpenCV (for image processing).
✅ CUDA (for parallelization).
✅ MSVC + nvcc (for compilation).
✅ CMake (for cross-platform build).

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⚙️ Usage

1. Add images to the ./img directory. Some sample images of various sizes are already included in the project.
2. Run the program.
3. Select an image from the list displayed in the terminal (images inside the ./img directory).
4. View the results in the ./img_results directory and check the execution time for both versions.

💡 The program will display execution times for both implementations!

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🗂️ Resulting Images:

The images will be saved in:

• .\img_results\color
• .\img_results\gray

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📄 Report

A copy of the report (in Italian) can be found here📄.

A copy of the presentation can be found here📄.

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🎉 Contributions

💡 This project was developed by Giovanni Stefanini, as part of the Parallel Computing course exam.

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👀 Visual Demo

Contrast enhancement using Histogram Equalization on sample images:

Original Input	Seq. Equalization	Parallel Equalization