Based on the legendary textbook by Rafael C. Gonzalez & Richard E. Woods
🚀 Quick Start • 📚 Features • 🎨 Gallery • 🔧 Installation • 📖 Documentation
DIP Bible is a comprehensive, interactive web application that brings the entire Digital Image Processing textbook to life! Whether you're a student learning the basics or a professional brushing up on advanced techniques, this tool provides hands-on experience with every major concept in the field.
💡 Why "Bible"? Because it's your complete reference guide - everything you need for Digital Image Processing in one beautiful, interactive application!
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Every major topic from the Gonzalez & Woods textbook, from fundamentals to neural networks Upload your image and see instant results with adjustable parameters Side-by-side comparisons, histograms, and beautiful visualizations |
Practical implementations of every important DIP technique Sliders, dropdowns, and controls to explore parameter effects Clean, intuitive interface built with Streamlit |
🔍 Click to expand full chapter list
- 📖 What is Digital Image Processing?
- 🎯 Application domains (Medical, Satellite, Industrial)
- 🖼️ Image properties and fundamentals
- 🔢 Sampling & Quantization
- 📊 Histograms & Statistics
- 🔗 Pixel Connectivity
- 📐 Image Interpolation (Nearest, Bilinear, Bicubic)
- 🎨 Image Negative, Log, Power Law (Gamma)
- 📊 Histogram Equalization & CLAHE
- 🔲 Smoothing Filters (Mean, Gaussian, Median)
- 📐 Sharpening Filters (Laplacian, Unsharp Masking, Sobel)
- 🌊 2D Fourier Transform
- ⬇️ Lowpass Filters (Ideal, Butterworth, Gaussian)
- ⬆️ Highpass Filters
- 🎯 Selective Filters (Bandreject, Notch)
- 🔊 Noise Models (Gaussian, Salt & Pepper, Poisson, Speckle)
- 🧹 Restoration Filters (Mean, Median, Adaptive)
- 🔍 Image Deblurring (Inverse, Wiener)
- 📊 Discrete Cosine Transform (DCT)
- 🌊 Discrete Wavelet Transform (Haar)
- 📈 Hadamard & Other Transforms
- 🎨 Color Models (RGB, HSV, LAB, YCrCb)
- 🌈 Color Transformations & Balance
- ✨ Color Enhancement
- 🔍 Color-based Segmentation
- 📉 Lossless Compression (RLE, Huffman)
- 📊 Lossy Compression (DCT-based)
- 🖼️ JPEG Simulation with Quality Control
- 🔷 Erosion, Dilation, Opening, Closing
- 🔧 Boundary Extraction, Hole Filling
- 🎨 Grayscale Morphology
- 🌊 Watershed Segmentation
- 📍 Edge Detectors (Sobel, Canny, Laplacian, Marr-Hildreth)
- 🎯 Thresholding (Manual, Otsu, Adaptive)
- 🔗 Hough Transform (Lines, Circles)
- 🌱 Region Growing & Clustering
- 🐍 Active Contours (Snakes)
- 📐 GrabCut Segmentation
- 🎯 Interactive Object Extraction
- 📏 Boundary Features (Area, Perimeter, Solidity)
- 🔲 Region Features (Texture, Moments)
- 🎯 Corner Detection (Harris, Shi-Tomasi, FAST)
- 🔍 SIFT Features
- 📊 Feature Space Visualization
- 🎯 K-Means Classification
- 🧠 Neural Network & CNN Concepts
- 🔬 Convolution Operation Demo
Python 3.8 or higher# Clone the repository
git clone https://github.com/yourusername/dip-bible.git
cd dip-bible
# Install dependencies
pip install -r requirements.txt
# Run the application
streamlit run app.pystreamlit>=1.28.0
numpy>=1.24.0
opencv-python>=4.8.0
pillow>=10.0.0
matplotlib>=3.7.0
scipy>=1.11.0
scikit-image>=0.21.0opencv-contrib-python>=4.8.0 # For SIFT features-
📤 Upload Image
Click "Upload Image" in the sidebar Support: PNG, JPG, JPEG, BMP, TIFF -
📚 Select Chapter
Choose from 13 comprehensive chapters Each chapter has multiple sub-topics -
🎛️ Adjust Parameters
Use sliders and controls See real-time results Compare before/after -
💾 Learn & Experiment
Read explanations Try different settings Understand the algorithms
| Original | Enhanced | Segmented | Features |
|---|---|---|---|
| 🖼️ Input | ✨ Processing | 🎯 Detection | 📊 Analysis |
Upload your own images and create amazing results!
| Category | Algorithms | Use Cases |
|---|---|---|
| 🎨 Enhancement |
• Histogram Equalization • Gamma Correction • Contrast Stretching • Sharpening Filters |
• Improve visibility • Adjust brightness • Enhance details |
| 🔍 Restoration |
• Noise Reduction • Deblurring • Wiener Filter • Median Filter |
• Remove noise • Fix blurry images • Restore quality |
| ✂️ Segmentation |
• Edge Detection • Thresholding • Watershed • GrabCut |
• Detect objects • Extract regions • Separate foreground |
| 🎯 Feature Extraction |
• Corner Detection • SIFT/ORB • Texture Analysis • Shape Descriptors |
• Object recognition • Image matching • Pattern analysis |
| 🗜️ Compression |
• JPEG Simulation • DCT Transform • Huffman Coding • Wavelet Compression |
• Reduce file size • Optimize storage • Transmission |
| Technology | Purpose | Version |
|---|---|---|
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Core Language | 3.8+ |
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Web Framework | 1.28+ |
 |
Image Processing | 4.8+ |
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Numerical Computing | 1.24+ |
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Scientific Computing | 1.11+ |
 |
Visualization | 3.7+ |
 |
Image Processing | 0.21+ |
- Start with Chapter 1 & 2 - Understand the basics
- Try Chapter 3 - Learn enhancement techniques
- Experiment with Chapter 9 - Visual morphological operations
- Move to Chapter 10 - Segmentation fundamentals
- Master Chapter 4 - Frequency domain filtering
- Explore Chapter 5 - Restoration techniques
- Study Chapter 7 - Color image processing
- Practice Chapter 12 - Feature extraction
- Deep dive into Chapter 6 - Wavelets & transforms
- Challenge yourself with Chapter 11 - Active contours
- Understand Chapter 13 - Pattern classification & CNNs
- Combine multiple techniques for complex problems
We love contributions! Here's how you can help:
Open an issue with details and steps to reproduce
Share your feature suggestions in discussions
- Fork the repository
- Create a feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Digital Image Processing (4th Edition)
Rafael C. Gonzalez and Richard E. Woods
Pearson/Prentice Hall, 2018
ISBN: 978-0133356724
- 📖 Gonzalez & Woods for the comprehensive textbook
- 🎨 Streamlit Team for the amazing framework
- 💻 OpenCV Community for powerful image processing tools
- 🌍 Open Source Community for inspiration and support
Made with ❤️ by the DIP Community
"The journey of a thousand pixels begins with a single upload." 🖼️