Image Noise Detection and Filtering

Description

This project implements an automated image noise detection and filtering system that identifies different types of noise (Gaussian, Salt & Pepper, Speckle, Periodic) and applies appropriate spatial and frequency domain filters. The system processes images, generates comparison visualizations, computes quality metrics (PSNR, SSIM), and produces a comprehensive analysis report.

How to Run

1. Create and activate virtual environment:

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

2. Install dependencies:

   pip install -r requirements.txt

3. Add your noisy images:

   • Place PNG images in the images/ folder

4. Run the program:

   python main.py

5. View results:

   • Filtered images: output/spatial/ and output/frequency/
   • Comparison images: output/comparison/
   • Histograms: output/histograms/
   • FFT spectrums: output/fft/
   • Metrics report: output/results.csv