AIU Image Toolkit

A comprehensive full-stack application for Image Processing and Cryptography based on CSE281 Lecture Notes.

Project Structure

├── backend/
│   ├── image_processor.py   # Core image processing functions
│   ├── app.py               # Flask REST API
│   └── requirements.txt     # Python dependencies
├── frontend/
│   ├── src/app/page.tsx     # Next.js frontend application
│   └── ...                  # Next.js project files
└── Lecture_*.pdf            # CSE281 Lecture notes

Features

Core Utilities

• to_grayscale: Convert RGB image to grayscale
• calc_psnr: Calculate Peak Signal-to-Noise Ratio between two images
• calc_correlation: Calculate correlation coefficient between two images

Spatial Domain Enhancement

• negative_transform: Image negative (S = 255 - r)
• log_transform: Logarithmic transformation (s = c * ln(1 + r))
• gamma_transform: Power-law/gamma transformation (s = c * r^γ)
• hist_equalization: Histogram equalization for contrast improvement
• binary_segmentation: Binary thresholding

Spatial Noise Filters

• add_salt_pepper: Add Salt & Pepper noise
• apply_gaussian_filter: Gaussian smoothing filter
• apply_median_filter: Median filter for noise removal

Frequency Domain Filtering

• get_frequency_spectrum: DFT magnitude spectrum visualization
• apply_ideal_lpf: Ideal Low-Pass Filter
• apply_ideal_hpf: Ideal High-Pass Filter
• apply_notch_filter: Notch filter for periodic noise removal

Cryptography

• xor_encrypt_decrypt: XOR-based image encryption/decryption

Setup Instructions

Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Flask server:

   python app.py

   The API will be available at http://localhost:5000

   For development with debug mode enabled:

   FLASK_DEBUG=true python app.py

Frontend Setup

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Create .env.local file:

   NEXT_PUBLIC_API_URL=http://localhost:5000
   

4. Run the development server:

   npm run dev

   The frontend will be available at http://localhost:3000

API Endpoints

Core Utilities

• POST /api/to-grayscale - Convert to grayscale
• POST /api/calc-psnr - Calculate PSNR
• POST /api/calc-correlation - Calculate correlation

Spatial Enhancement

• POST /api/negative-transform - Apply negative transform
• POST /api/log-transform - Apply log transform
• POST /api/gamma-transform - Apply gamma transform
• POST /api/hist-equalization - Apply histogram equalization
• POST /api/binary-segmentation - Apply binary segmentation

Noise Filters

• POST /api/add-salt-pepper - Add salt & pepper noise
• POST /api/gaussian-filter - Apply Gaussian filter
• POST /api/median-filter - Apply median filter

Frequency Domain

• POST /api/frequency-spectrum - Get frequency spectrum
• POST /api/ideal-lpf - Apply ideal low-pass filter
• POST /api/ideal-hpf - Apply ideal high-pass filter
• POST /api/notch-filter - Apply notch filter

Cryptography

• POST /api/xor-encrypt - Encrypt with XOR
• POST /api/xor-decrypt - Decrypt with XOR
• POST /api/attack-scenario - Run complete attack-decryption scenario

Documentation

• GET /api/docs - Get API documentation
• GET /api/health - Health check endpoint

Attack-Decryption Scenario

The toolkit includes a complete demonstration of the attack-decryption scenario:

1. Encrypt: Original image is encrypted using XOR with a secret key
2. Attack: Salt & Pepper noise is added to the encrypted image
3. Decrypt: The attacked image is decrypted using the same key
4. Evaluate: PSNR and correlation metrics measure image quality degradation

Academic References

All implementations are based on CSE281 Lecture Notes:

• Lecture 4: Image Negatives, Binary Segmentation
• Lecture 5: Grayscale Conversion, Log Transform, Gamma Transform, Histogram Equalization
• Lecture 6: Salt & Pepper Noise, Gaussian Filter, Median Filter
• Lecture 8: DFT, Frequency Filters (LPF, HPF, Notch), XOR Encryption, PSNR, Correlation

License

Educational use - AIU CSE281 Course Materials