Fourier Image Processing

A Fourier Transform–based image processing project demonstrating frequency-domain filtering, compression, restoration, and feature extraction. Includes FFT visualizations, motion blur simulation, Wiener filtering, and contrast analysis.

Features

• Fourier Transform Analysis: Visualize and understand frequency-domain representations of images
• Image Compression: Demonstrate lossy compression using FFT coefficient thresholding
• Image Enhancement: Apply frequency-domain filtering for contrast and sharpness improvements
• Motion Blur Simulation: Create and analyze motion blur effects in the frequency domain
• Wiener Filtering: Implement restoration techniques for degraded images
• Feature Extraction: Extract meaningful features from frequency-domain data

Project Structure

├── final_commented.m          # Main script with detailed comments
├── final_f.m                  # Final implementation
├── fourier_f.m                # Core Fourier transform functions
├── fourier_fd.m               # Frequency domain operations
├── fouriertransform_compress_enhance.m  # Compression and enhancement pipeline
├── Fourier_bases.mlx          # MATLAB Live Script for Fourier bases visualization
├── plotcircle.m               # Utility for circular frequency visualization
├── *.fig                      # MATLAB figure files with results
└── README.md

MATLAB Figure Files

• original.fig - Original input image
• compressed.fig, compressed50.fig - Compression results at different levels
• enhanced.fig, enhanced2.fig, enhanced3.fig - Various enhancement outputs
• en_original.fig, en_greyscale.fig - Greyscale conversions
• en_colored_enhanced.fig, en_enhanced.fig - Color and enhanced versions

Requirements

• MATLAB R2019b or later
• Image Processing Toolbox (recommended)

Usage

1. Open MATLAB and navigate to the project directory
2. Run final_commented.m for a step-by-step walkthrough
3. Use fouriertransform_compress_enhance.m for the complete pipeline
4. Explore Fourier_bases.mlx for interactive Fourier basis visualizations

Theory

The project demonstrates key concepts in frequency-domain image processing:

1. 2D Discrete Fourier Transform (DFT): Converting spatial domain images to frequency domain
2. Frequency Filtering: Low-pass, high-pass, and band-pass filtering
3. Compression: Removing high-frequency components for data reduction
4. Enhancement: Amplifying specific frequency bands for better contrast

License

This project is for educational purposes.

Author

Naveen Babu Kishore B Koushal Reddy Sai Charan