https://github.com/abhinavexists/wavelet-transforms
Image denoising using wavelet transforms, featuring multiple thresholding methods, color space processing.
https://github.com/abhinavexists/wavelet-transforms
denoising flask python wavelet-transform
Last synced: 11 months ago
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Image denoising using wavelet transforms, featuring multiple thresholding methods, color space processing.
- Host: GitHub
- URL: https://github.com/abhinavexists/wavelet-transforms
- Owner: Abhinavexists
- License: mit
- Created: 2025-04-19T15:14:14.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-04-19T17:35:23.000Z (11 months ago)
- Last Synced: 2025-04-24T00:58:28.338Z (11 months ago)
- Topics: denoising, flask, python, wavelet-transform
- Language: Python
- Homepage:
- Size: 28.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Wavelet Transform Image Denoising
A powerful and flexible web application for image denoising using wavelet transforms, featuring multiple thresholding methods, color space processing, and comparative analysis capabilities.
## Theory
### Wavelet Transform in Image Denoising
Wavelet transforms are powerful tools for image processing, particularly in denoising applications. They work by:
1. **Decomposition**: Breaking down the image into different frequency components while preserving spatial information
2. **Thresholding**: Removing noise by modifying wavelet coefficients
3. **Reconstruction**: Rebuilding the image from the modified coefficients
This project implements three sophisticated thresholding methods:
- **VisuShrink**: Universal threshold based on image size
- **BayesShrink**: Adaptive threshold using Bayesian estimation
- **SureShrink**: Stein's Unbiased Risk Estimate for optimal threshold selection
### Color Space Processing
For color images, processing in YCbCr color space often yields better results than RGB:
- Y (Luma): Carries brightness information
- Cb, Cr (Chroma): Carry color information
- Allows for different denoising strengths for brightness and color components
## Results
### Example Denoising Results
Denoising Sample
## Installation
1. Clone the repository:
```bash
git clone https://github.com/Abhinavexists/Wavelet-Transforms.git
cd wavelet-transforms
```
2. Create and activate a virtual environment:
```bash
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
### Web Interface
1. Start the Flask application:
```bash
python app.py
```
2. Open your browser and navigate to `http://localhost:5000`
3. Use the interface to:
- Upload images via drag-and-drop or file selection
- Adjust denoising parameters
- Compare different methods
- Download processed images
### Parameters
- **Wavelet Type**: Choose from various wavelet families
- `bior4.4` (Default): Biorthogonal 4.4
- `haar`: Haar wavelet
- `db4`: Daubechies 4
- `sym4`: Symlets 4
- `coif3`: Coiflets 3
- **Thresholding Method**:
- `BayesShrink` (Default): Adaptive threshold
- `VisuShrink`: Universal threshold
- `SureShrink`: SURE-based threshold
- **Decomposition Level**: 1-4 (auto-selected by default)
- **Color Space**: RGB or YCbCr
- **Noise Addition**: Optional noise simulation
## Project Structure
```
wavelet-transforms/
├── app.py # Flask application
├── transform.py # Wavelet transform implementation
├── static/
│ ├── css/ # Stylesheets
│ └── js/ # JavaScript files
├── templates/ # HTML templates
├── uploads/ # Temporary image storage
└── output/ # Processed images
```
## Dependencies
- Python 3.9+
- NumPy
- PyWavelets
- scikit-image
- OpenCV
- Flask
- Additional requirements in `requirements.txt`
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License - see the LICENSE file for details.