Published June 3, 2025 | Version v1
Preprint Open

Wavelet-Based Denoising of Images and Audio Signals Using Pywavelet and Other Python Libraries

Creators

  • 1. EDMO icon University of Illinois at Chicago

Description

This project explores image and audio denoising using wavelet transform techniques in Python.
It employs Discrete Wavelet Transform (DWT) and both soft and hard thresholding for noise suppression.
Custom algorithms were developed and evaluated using PSNR and SSIM metrics.
Visualization and audio playback demonstrate the effectiveness of the denoising process.
The implementation leverages PyWavelets, SciPy, OpenCV, and Matplotlib for end-to-end processing.

Files

Wavelet-Based Denoising of Images and Audio Signals Using Pywavelet and Other Python Libraries.pdf

Files (1.6 MB)

Additional details

Software

Repository URL
https://github.com/SafersTechnologies?tab=repositories
Programming language
Python
Development Status
Active

References

  • [1] M. Smith and P. Jones, "Wavelet transforms and denoising algorithms," in Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1998, vol. 3, pp. 1845 1848, doi: 10.1109/ICASSP.1998.751603. [2] D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis. Cambridge University Press, 2006. [3] K. Bnou, S. Raghay, and A. Hakim, "A wavelet denoising approach based on unsupervised learning model," EURASIP Journal on Advances in Signal Processing, vol. 2020, no. 1, p. 36, Jul. 2020, doi: 10.1186/s13634-020-00693-4. [4] Y. Zhang, X. Li, and L. Wang, "Research on image denoising in edge detection based on wavelet transform," Applied Sciences, 2023.K. Elissa, "Title of paper if known," unpublished. [5] A. K. Singh and S. K. Singh, "Review of wavelet denoising algorithms," Multimedia Tools and Applications, 2023, doi: 10.1007/s11042-023-15127-0. [6] K. Naveed and N. ur Rehman, "Wavelet-based multivariate signal denoising using Mahalanobis distance and EDF statistics," arXiv preprint, May 2020. Available: https://arxiv.org/abs/2005.11616. [7] Y. Peng, Y. Cao, S. Liu, J. Yang, and W. Zuo, "Progressive training of multi-level wavelet residual networks for image denoising," arXiv preprint, Oct. 2020.Available: https://arxiv.org/abs/2010.12422.