Image Histogram and Contrast Equalization Explorer

Added Jun 2025 3 design docs

A histogram is the single most informative summary of an image's exposure and contrast, and histogram equalization is the classic remedy for washed-out or underexposed pictures, yet many students use both without ever understanding what the distribution actually shows. Connecting the shape of a histogram to the look of an image is a foundational skill for anyone working in imaging. The intern builds a Streamlit tool using OpenCV that makes the connection visible. Users upload an image and the app plots its intensity histogram, for grayscale and per color channel, directly alongside the picture. Applying histogram equalization then transforms the image and redraws the distribution, so users see simultaneously how the pixel values spread out and how the visual contrast improves. The intern implements before-and-after comparison views, handles color images correctly by equalizing luminance rather than raw channels, and adds explanatory notes on when equalization helps and when it amplifies noise. The project teaches the intern to read images statistically rather than just visually, builds practical skills with OpenCV histogram computation and Python data visualization, and demonstrates the ability to turn a core image enhancement concept into an interactive, self-explanatory learning tool.

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