{"id":27644711,"url":"https://github.com/abhinavexists/wavelet-transforms","last_synced_at":"2026-05-01T19:33:08.697Z","repository":{"id":288807745,"uuid":"969180356","full_name":"Abhinavexists/Wavelet-Transforms","owner":"Abhinavexists","description":"Image denoising using wavelet transforms, featuring multiple thresholding methods, color space processing.","archived":false,"fork":false,"pushed_at":"2025-04-19T17:35:23.000Z","size":29938,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-24T00:58:28.338Z","etag":null,"topics":["denoising","flask","python","wavelet-transform"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Abhinavexists.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-19T15:14:14.000Z","updated_at":"2025-04-19T17:35:26.000Z","dependencies_parsed_at":"2025-04-19T20:30:14.130Z","dependency_job_id":null,"html_url":"https://github.com/Abhinavexists/Wavelet-Transforms","commit_stats":null,"previous_names":["abhinavexists/wavelet-transforms"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhinavexists%2FWavelet-Transforms","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhinavexists%2FWavelet-Transforms/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhinavexists%2FWavelet-Transforms/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhinavexists%2FWavelet-Transforms/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Abhinavexists","download_url":"https://codeload.github.com/Abhinavexists/Wavelet-Transforms/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250540999,"owners_count":21447427,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["denoising","flask","python","wavelet-transform"],"created_at":"2025-04-24T00:58:33.081Z","updated_at":"2026-05-01T19:33:08.649Z","avatar_url":"https://github.com/Abhinavexists.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Wavelet Transform Image Denoising\n\nA powerful and flexible web application for image denoising using wavelet transforms, featuring multiple thresholding methods, color space processing, and comparative analysis capabilities.\n\n![Frontend Interface](Frontend.png)\n\n## Theory\n\n### Wavelet Transform in Image Denoising\n\nWavelet transforms are powerful tools for image processing, particularly in denoising applications. They work by:\n\n1. **Decomposition**: Breaking down the image into different frequency components while preserving spatial information\n2. **Thresholding**: Removing noise by modifying wavelet coefficients\n3. **Reconstruction**: Rebuilding the image from the modified coefficients\n\nThis project implements three sophisticated thresholding methods:\n\n- **VisuShrink**: Universal threshold based on image size\n- **BayesShrink**: Adaptive threshold using Bayesian estimation\n- **SureShrink**: Stein's Unbiased Risk Estimate for optimal threshold selection\n\n### Color Space Processing\n\nFor color images, processing in YCbCr color space often yields better results than RGB:\n- Y (Luma): Carries brightness information\n- Cb, Cr (Chroma): Carry color information\n- Allows for different denoising strengths for brightness and color components\n\n## Results\n\n### Example Denoising Results\n\n![Results](Results.png)\n\nDenoising Sample\n![Comparision](output/comparison_1.png)\n\n## Installation\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/Abhinavexists/Wavelet-Transforms.git\ncd wavelet-transforms\n```\n\n2. Create and activate a virtual environment:\n```bash\npython -m venv .venv\nsource .venv/bin/activate  # On Windows: .venv\\Scripts\\activate\n```\n\n3. Install dependencies:\n```bash\npip install -r requirements.txt\n```\n\n## Usage\n\n### Web Interface\n\n1. Start the Flask application:\n```bash\npython app.py\n```\n\n2. Open your browser and navigate to `http://localhost:5000`\n\n3. Use the interface to:\n   - Upload images via drag-and-drop or file selection\n   - Adjust denoising parameters\n   - Compare different methods\n   - Download processed images\n\n### Parameters\n\n- **Wavelet Type**: Choose from various wavelet families\n  - `bior4.4` (Default): Biorthogonal 4.4\n  - `haar`: Haar wavelet\n  - `db4`: Daubechies 4\n  - `sym4`: Symlets 4\n  - `coif3`: Coiflets 3\n\n- **Thresholding Method**:\n  - `BayesShrink` (Default): Adaptive threshold\n  - `VisuShrink`: Universal threshold\n  - `SureShrink`: SURE-based threshold\n\n- **Decomposition Level**: 1-4 (auto-selected by default)\n- **Color Space**: RGB or YCbCr\n- **Noise Addition**: Optional noise simulation\n\n## Project Structure\n\n```\nwavelet-transforms/\n├── app.py              # Flask application\n├── transform.py        # Wavelet transform implementation\n├── static/\n│   ├── css/            # Stylesheets\n│   └── js/             # JavaScript files\n├── templates/          # HTML templates\n├── uploads/            # Temporary image storage\n└── output/             # Processed images\n```\n\n## Dependencies\n\n- Python 3.9+\n- NumPy\n- PyWavelets\n- scikit-image\n- OpenCV\n- Flask\n- Additional requirements in `requirements.txt`\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhinavexists%2Fwavelet-transforms","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhinavexists%2Fwavelet-transforms","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhinavexists%2Fwavelet-transforms/lists"}