Digital-Image-Processing

This project demonstrates fundamental and widely-used techniques in Digital Image Processing (DIP) using Python and OpenCV. It is divided into four key areas: image enhancement, segmentation, spatial filtering, and histogram equalization. Each section includes practical implementations of popular algorithms used in image analysis and preprocessing.

📁 Project Structure

1. Image Enhancement Techniques

Enhances the visual appearance of an image using various pixel-level operations:

    Negative (Inverse) Transformation
    
    Logarithmic Transformation
    
    Gamma (Power-Law) Correction

These techniques are useful for improving brightness, contrast, and visibility in different lighting conditions.

2. Image Segmentation

Divides an image into regions or objects for easier analysis:

    Canny Edge Detection
    
    Otsu’s Thresholding

These methods help in object detection, contour extraction, and image analysis tasks.

3. Spatial Domain Filters

Applies filters directly on image pixels to enhance or suppress features:

    Low-Pass Filtering (Smoothing)
    
    High-Pass Filtering (Edge Enhancement)
    
    Gaussian Filter
    
    Median Filter

Used for noise reduction, detail enhancement, and feature sharpening.

4. Histogram Equalization

Improves the contrast of images using:

    Global Histogram Equalization (OpenCV)

This technique redistributes pixel intensities to enhance image contrast, especially useful for low-contrast or poorly lit images.