This project showcases the implementation of the histogram equalization method using C and Python. The method is a popular technique for improving image contrast by redistributing pixel intensity values. This demo includes visualizations created with Matplotlib to display the differences in image contrast before and after applying the histogram equalization method.
Histogram equalization is an image processing technique used to enhance the contrast of an image by equalizing its intensity distribution. It does this by spreading out the most frequent intensity values, making the image histogram more uniform. This results in an image with improved contrast and enhanced details in both bright and dark regions.
Histogram equalization is a method in image processing of contrast adjustment using the image's histogram.
To run the demo, follow these steps:
- Clone the repository to your local machine:
git clone https://github.com/yourusername/histogram-equalization-demo.git- Change into the project directory:
cd histogram-equalization-demo- Install the required Python dependencies:
pip install -r requirements.txt- Compile the C implementation:
gcc -o equalization histogram_equalization.c- Run the Python script with the desired input image:
python histogram_equalization.py input_image.jpg
- View the generated output images and histogram visualizations in the output folder.
To learn more about histogram equalization and image processing techniques, check out the following resources:
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Thank you for visiting my Histogram Equalization repository, and I hope you enjoy learning! 😃