THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

External Links

Hongjin Ma

,

Chenxin Zhang

,

Weiwei Zhang

TWO STAGES NON-LOCAL MEANS ALGORITHM BASED ON WAVELET TRANSFORM

ABSTRACT
This paper presents an innovative wavelet transform-based two-stage non-local means algorithm that offers a new approach to image de-noising. It first decomposes the noisy image into four sub-images with different frequencies, which provides a more detailed analysis. Then, it fuses three high frequency sub-images into one high frequency sub-image, which enhances the image quality. The low-frequency sub-image and the fused high frequency sub-image are de-noised using the non-local means algorithm, which is a highly effective and robust de-noising technique. Furthermore, the de-noised low-frequency sub-image and the high frequency sub-image are merged to form the initial de-noised image. Finally, the initial de-noised image is de-noised again using the non-local means algorithm to produce the final de-noised image. The proposed algorithm is able to effectively remove noise while preserving edges and details.
KEYWORDS
image de-noising, non-local means, two stages de-noising, wavelet transform
PAPER SUBMITTED: 2024-05-08
PAPER REVISED: 2024-07-07
PAPER ACCEPTED: 2024-07-07
PUBLISHED ONLINE: 2025-07-06
DOI REFERENCE: https://doi.org/10.2298/TSCI2503015M
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2025, VOLUME 29, ISSUE Issue 3, PAGES [2015 - 2021]
REFERENCES
  1. Xu, L. P., Digital Image Processing, Publishing House of Science, Beijing, 2007, pp. 86-88
  2. Huang, H. C., Lee, T. C. M., Data Adaptive Median Filters for Signal and Image De-Noising Using a Generalized SURE Criterion, IEEE Signal Processing Letters, 13 (2006), 9, pp. 561-564
  3. Yuan, S. Q., Tan, Y. H., Impulse Noise Removal by a Global-Local Noise Detector and Adaptive Median Filter, Signal Processing, 86 (2006), 8, pp. 2123-2128
  4. Huang, X. D., Woolsey, G. A., Image De-Noising Using Wiener Filtering and Wavelet Thresholding, Proceedings, IEEE Multimedia and Expo, New York, USA, IEEE, 2000, pp. 1759-1762
  5. Buades, A., et al., A Non-Local Algorithm for Image De-Noising, Proceedings, Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, Cal., USA, IEEE, 2005, II, pp. 60-65
  6. Dabov, K., et al., Image De-Noising by Sparse 3-D Transform-Domain Collaborative Filtering, IEEE Transactions on Image Processing, 16 (2007), 8, pp. 2080-2095
  7. Tasdizen, T., Principal Neighborhood Dictionaries for Nonlocal Means Image De-Noising, IEEE Transactions on Image Processing, 18 (2009), 12, pp. 2649-2660
  8. Grewenig, S., et al., Rotationally Invariant Similarity Measures for Nonlocal Image De-Noising, Journal of Visual Communication and Image Representation, 22 (2011), 2, pp. 117-130
  9. Wu, Y., et al., Probabilistic Nonlocal Means, IEEE Signal Processing Letters, 20 (2013), 8, pp. 763-766
  10. Zhang, X. D., et al., Exploit the Similarity while Preserving the Difference for Nonlocal Image De-Noising, Scientia Sinica (Informationis), 43 (2013), 7, pp. 907-919
  11. Jacques, F., Parameter-Free Fast Pixelwise Non-Local Means De-Noising, Image Processing on Line, 4 (2014), Nov., pp. 300-326
  12. Cai, B., et al., An Improved Non-Local Means De-Noising Algorithm, Pattern Recognition and Artificial Intelligence, 29 (2016), 1, pp. 1-10
  13. Nguyen, M. P., and Chun, S. Y., Bounded Self-Weights Estimation Method for Non-Local Means Image De-Noising Using Minimax Estimators, IEEE Transactions on Image Processing, 26 (2017), 4, pp. 1637-1649
  14. Verma, R., Pandey, R., Grey Relational Analysis Based Adaptive Smoothing Parameter for Non-Local Means Image De-Noising, Multimedia Tools and Applications, 77 (2018), 19, pp. 25919-25940
  15. Bo, L., et al., A Novel and Fast Nonlocal Means De-Noising Algorithm Using a Structure Tensor, The Journal of Supercomputing, 75 (2019), 2, pp. 770-782
  16. Xing, X. X., et al., Asymptotic Non-local Means Image De-noising Algorithm. Acta Automatica Sinica, 46 (2020), 9, pp. 1952-1960
  17. Singh, I., & Preeti, Chebyshev Wavelets Based Technique for Numerical Differentiation, Advances in Differential Equations and Control Processes, 30 (2023), 1, pp. 15-25
PDF VERSION [DOWNLOAD]
Two stages non-local means algorithm based on wavelet transform

2025 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence