Digital Image Denoising Techniques in Wavelet Domain with another Filter: A review


  • Barwar M. Ferzo Department of IT, Duhok Polytechnic University, Duhok, Kurdistan of Iraq
  • Firas M. Mustafa Department of Communication, Nawroz University and (Duhok Polytechnic University), Duhok, Kurdistan Region of Iraq



Image denoising is a challenging issue found in diverse image processing and computer vision problems. There are various existing methods investigated to denoising image. The essential characteristic of a successful model that denoising image is that it should eliminate noise as far as possible and edges preserving and necessary image information by improving visual quality. This paper presents a review of some significant work in the field of image denoising based on that the denoising methods can be roughly classified as spatial domain methods, transform domain methods, or can mix both to get the advantages of them. This work tried to focus on this mixing between using wavelet transform and the filters in spatial domain to show spatial domain. There have been numerous published algorithms, and each approach has its assumptions, advantages, and limitations depending on the various merits and noise. An analyzing study has been performed comparative in their methods to achieve the denoising algorithms, filtering approach and wavelet-based approach. Standard measurement parameters have been used to compute results in some studies to evaluate techniques while other methods applied new measurement parameters to evaluate the denoising techniques.


Download data is not yet available.


1. Kommineni, V.R.R. and H.K. Kalluri, Image Denoising Techniques. International Journal of Recent Technology and Engineering (IJRTE), 2019. 7(5S4).
2. Koranga, P., et al., Image Denoising Based on Wavelet Transform using Visu Thresholding Technique. International Journal of Mathematical, Engineering and Management Sciences, 2018.
3. Singh, L. and R. Janghel, Harmony Search and Nature Inspired Optimization Algorithms "Image Denoising Techniques: A Brief Survey". Advances in Intelligent Systems and Computing 741, springer, 2018. 731-740.
4. Alisha, P.B. and G.S. K, Image Denoising Techniques-An Overview. Journal of Electronics and Communication Engineering (IOSR-JECE), 2016. 11(1): p. 78-84.
5. Fan, L., et al., Brief review of image denoising techniques. Springer Singapore (Visual Computing for Industry, Biomedicine, and Art), 2019.
6. Song, Q., et al., Image Denoising Based on Mean Filter and Wavelet Transform. IEEE 4th International Conference on Advanced Information Technology and Sensor Application (AITS), 2015.
7. Wang, G., Z. Wang, and J. Liu, A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection. Mathematical Problems in Engineering, 2017.
8. Xiaoa, F. and Y. Zhanga, A Comparative Study on Thresholding Methods in Waveletbased Image Denoising. Elsevier (Advanced in Control Engineeringand Information Science), 2011. 15.
9. Kethwas, A. and B. Jharia, Image de-noising using Fuzzy and Wiener filter in Wavelet domain. IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2015.
10. Jagadesh, T. and R.J. Rani, A novel speckle noise reduction in biomedical images using PCA and wavelet transform. IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016.
11. Agarwal, S.K. and P. Kumar, Denoising of A Mixed Noise Color Image Through Special Filter. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016. 9(1): p. 159-176.
12. Kaur, G. and R. Kaur, Image De-Noising Using Wavelet Transform and Various Filters. International Journal of Research in Computer Science, 2012. 2(2): p. 15-21.
13. Kumar, P. and S.K. Agarwal, A Color Image Denoising By Hybrid Filter for Mixed Noise. International Journal of Current Engineering and Technology (ijcet), 2015. 5.
14. Lin, L., An Effective Denoising Method for Images Contaminated with Mixed Noise Based on Adaptive Median Filtering and Wavelet Threshold Denoising. J Inf Process Syst (JIPS), 2017. 14(2): p. 539-551.
15. Fan, L., et al., Brief review of image denoising techniques. Visual Computing for Industry, Biomedicine, and Art, 2019.
16. Guhathakurta, R., Denoising of Image : A Wavelet Based Approach. 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), 2017.
17. Chen, B., et al., Adaptive Wavelet Filter With Edge Compensation for Remote Sensing Image Denoising. IEEE, 2019. 7: p. 91966 - 91979.
18. Qian, Y., Removing of Salt-and-pepper Noise in Images Based on Adaptive Median Filtering and Improved Threshold Function. 2019 Chinese Control And Decision Conference (CCDC), 2019.
19. Nishu, I.Z., et al., A New Image Despeckling Method by SRAD Filter and Wavelet Transform Using Bayesian Threshold. IEEE International Conference on Electrical, Computer and Communication Engineering (ECCE), 2019.
20. Qian, Y., Image Denoising Algorithm Based on Improved Wavelet Threshold Function and Median Filter. IEEE 18th International Conference on Communication Technology (ICCT), 2018.
21. Longkumer, M. and H. Gupta, Denoising of Images Using Wavelet Transform,Weiner Filter and Soft Thresholding. International Research Journal of Engineering and Technology (IRJET), 2018. 5(6).
22. Aghabalaei, A., et al., Speckle Noise Reduction of Time Series SAR Images Based On Wavelet Transform and Kalman Filter. IGARSS 2018 - IEEE International Geoscience and Remote Sensing Symposium, 2018.
23. Choi, H. and J. Jeong, Despeckling Images Using a Preprocessing Filter and Discrete Wavelet Transform-Based Noise Reduction Techniques. IEEE Sensors Journal 2018. 18(8): p. 3131 - 3139.
24. Dass, R., Speckle Noise Reduction of Ultrasound Images Using BFO Cascaded with Wiener Filter and Discrete Wavelet Transform in Homomorphic Region. Procedia Computer Science, International Conference on Computational Intelligence and Data Science (ICCIDS 2018). 2018. 132: p. 1543-1551.
25. Tayade, P.M. and S.P. Bhosale, Medical Image Denoising and Enhancement using DTCWT and Wiener filter. International Journal of Advance Research, Ideas and Innovations in Technology (IJARIIT), 2018. 4(4).
26. Luo, P., et al., CT Image Denoising Using Double Density Dual Tree Complex Wavelet with Modified Thresholding. IEEE 2nd International Conference on Data Science and Business Analytics (ICDSBA), 2018.
27. Kannan, K., A New Hybrid Image Denoising Algorithm Based on Wiener Filter and Wavelet Thresholding. International Journal of Research in Information Technology, 2017. 1(2): p. 37-45.
28. Chithra, K. and T. Santhanam, Hybrid Denoising Technique for Suppressing Gaussian Noise in Medical Images. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 2017.
29. Ramadhan, A., F. Mahmood, and A. Elci, Image Denoising by Median Filter In Wavelet Domain. The International Journal of Multimedia & Its Applications (IJMA), 2017. 9(1).
30. Majeeth, S.S. and C.N.K. Babu, A Novel Algorithm to Remove Gaussian Noise in an Image. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2017.
31. Wang, J., et al., Wiener filter-based wavelet domain denoising. Elsevier - Displays, 2016. 46: p. 37-41.
32. Ismael, S.H., D.F.M. Mustafa, and D.İ.T. OKÜMÜŞ, A New Approach of Image Denoising Based on Discrete Wavelet Transform. IEEE World Symposium on Computer Applications & Research, 2016.
33. Diwakar, M. and M. Kumar, Edge Preservation Based CT Image Denoising Using Wiener Filtering and Thresholding in Wavelet Domain. IEEE Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), 2016.
34. Mohan, R., S. Mridula, and P. Mohanan, Speckle Noise Reduction in Images using WienerFiltering and Adaptive Wavelet Thresholding. IEEE Region 10 Conference (TENCON), 2016.
35. Saluja, R. and A. Boyat, Wavelet based Image Denoising using Weighted Highpass Filtering Coefficients and Adaptive Wiener Filter. IEEE International Conference on Computer, Communication and Control (IC4-2015), 2015.
36. Naimi, H., et al., Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter. Journal of King Saud University – Computer and Information Sciences, 2015.



How to Cite

Ferzo, B. M., & Mustafa, F. M. (2020). Digital Image Denoising Techniques in Wavelet Domain with another Filter: A review. Academic Journal of Nawroz University, 9(1), 158–176.