Digital Image Processing Project

Project Overview:

As part of our coursework at the Faculty of Artificial Intelligence, we developed a comprehensive Digital Image Processing application — an all-in-one GUI-based tool that brings core image processing concepts to life.

This project transforms theory into an interactive visual experience, allowing users to apply and observe real-time image transformations. ?

Core Functionalities:

1. Point Operations

Addition, Subtraction, Division, Complement

2. Color Image Manipulation

Lighting adjustment (e.g., modify red channel)

Channel swapping (e.g., R → G)

Channel removal (e.g., remove red)

3. Histogram Techniques

Histogram Stretching (Grayscale)

Histogram Equalization (Grayscale)

4. Neighborhood-Based Filters

Linear: Average, Laplacian

Non-linear: Max, Min, Median, Mode

5. Image Restoration

Salt & Pepper noise removal (Average, Median, Outlier)

Gaussian noise reduction (Image Averaging, Average Filter)

6. Image Segmentation

Global Thresholding

Automatic Thresholding

Adaptive Thresholding

7. Edge Detection

Sobel Edge Detector

8. Mathematical Morphology

Dilation

Erosion

App Interaction:

For every operation, users can upload an image, apply the technique, and view the result alongside the original image using subplot visualization — making it easy to compare before and after effects.

? Tech Stack:

Python – CustomTkinter – OpenCV – NumPy – Matplotlib