Histogram Analysis on Gray Level Image
View on GitHubImage Processing · C
Overview
A C implementation of fundamental histogram analysis techniques applied to grayscale images. The project covers computing intensity histograms, histogram equalization for contrast enhancement, and histogram stretching — all implemented from scratch without any image processing libraries.
This project sits at the foundation of computer vision and image processing — understanding how pixel intensity distributions work is essential before moving to more advanced techniques like edge detection, segmentation, or deep learning-based analysis.
Techniques Implemented
Histogram Computation
Count the frequency of each intensity level (0–255) across all pixels in the grayscale image, producing a distribution that characterizes the image's overall brightness and contrast.
Histogram Equalization
Redistribute pixel intensities using the cumulative distribution function (CDF) to spread them more evenly across the full range — dramatically improving contrast in low-quality or underexposed images.
Histogram Stretching
Linearly scale pixel intensities so the darkest pixel maps to 0 and the brightest to 255, maximizing the dynamic range of the image.
Statistical Analysis
Compute mean, variance, and standard deviation of pixel intensities to quantitatively describe image characteristics.
Tech Stack
Key Learnings
- ▸Learned how images are represented as 2D arrays of pixel intensities and how to manipulate them at the byte level in C.
- ▸Understood the mathematical relationship between spatial domain operations and their statistical effects on image quality.
- ▸Histogram equalization is one of the most elegant algorithms in image processing — a simple idea that produces dramatically visible improvements.
- ▸This foundational knowledge directly feeds into my current work applying deep learning to sonar image analysis — understanding raw pixel distributions is essential for data preprocessing and augmentation.