Hybrid Image Denoising Using Wavelet Transform and Deep Learning
DOI:
https://doi.org/10.4108/airo.7486Keywords:
Convolutional Neural Network, Deep Learning, Hybrid Approach, Image Denoising, Wavelet TransformAbstract
In this paper, we propose a hybrid image denoising method that combines wavelet transform and deep learning techniques to effectively remove noise from digital images. The wavelet transform is applied to each color channel of the noisy image, decomposing it into different frequency components. The approximation coefficients are then denoised using a convolutional neural network (CNN), specifically designed for this task. The denoised coefficients are subsequently reconstructed to form the final denoised image. Our experimental results demonstrate that this hybrid approach outperforms traditional denoising methods, achieving superior noise reduction while preserving image details. The proposed method is validated using synthetic noisy images, and the results are visually and quantitatively evaluated to confirm its effectiveness.
Downloads
References
1. Agarwal S, Singh OP, Nagaria D. Analysis and comparison of wavelet transforms for denoising MRI image. Biomedical and Pharmacology Journal. 2017;10(2):831–6.
2. Arab H, Ghaffari I, Evina RM, Tatu SO, Dufour S. A hybrid LSTM-ResNet deep neural network for noise reduction and classification of V-band receiver signals. IEEE Access. 2022; 10:14797–806.
3. Qin X, Lai C, Pan Z, Pan M, Xiang Y, Wang Y. Recognition of Abnormal-Laying Hens Based on Fast Continuous Wavelet and Deep Learning Using Hyperspectral Images. Sensors. 2023;23(7):3645.
4. Anwar S, Barnes N. Real image denoising with feature attention. In: Proceedings of the IEEE/CVF international conference on computer vision. 2019. p. 3155–64.
5. Bao Z, Zhang G, Xiong B, Gai S. New image denoising algorithm using monogenic wavelet transform and improved deep convolutional neural network. Multimed Tools Appl. 2020;79(11):7401–12.
6. Tian C, Zheng M, Zuo W, Zhang B, Zhang Y, Zhang D. Multi-stage image denoising with the wavelet transform. Pattern Recognit. 2023; 134:109050.
7. Averbuch A, Neittaanmäki P, Zheludev V, Salhov M, Hauser J. An hybrid denoising algorithm based on directional wavelet packets. Multidimens Syst Signal Process. 2022;33(4):1151–83.
8. Koranga P, Singh G, Verma D, Chaube S, Kumar A, Pant S. Image denoising based on wavelet transform using Visu thresholding technique. International Journal of Mathematical, Engineering and Management Sciences. 2018;3(4):444.
9. Kumar A, Tomar H, Mehla VK, Komaragiri R, Kumar M. Stationary wavelet transform based ECG signal denoising method. ISA Trans. 2021; 114:251–62.
10. Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing. 2017;26(7):3142–55.
11. Jifara W, Jiang F, Rho S, Cheng M, Liu S. Medical image denoising using convolutional neural network: a residual learning approach. J Supercomput. 2019;75:704–18.
12. Cui J, Gong K, Guo N, Wu C, Meng X, Kim K, et al. PET image denoising using unsupervised deep learning. Eur J Nucl Med Mol Imaging. 2019; 46:2780–9.
13. Zhou X, Zhou H, Wen G, Huang X, Lei Z, Zhang Z, et al. A hybrid denoising model using deep learning and sparse representation with application in bearing weak fault diagnosis. Measurement. 2022; 189:110633.
14. Tian C, Zheng M, Zuo W, Zhang B, Zhang Y, Zhang D. Multi-stage image denoising with the wavelet transform. Pattern Recognit. 2023; 134:109050.
15. Davari M, Harooni A, Nasr A, Savoji K, Soleimani M. Improving recognition accuracy for facial expressions using scattering wavelet. EAI Endorsed Transactions on AI and Robotics. 2024;3.
16. Jiang H, Fu W. Computer vision recognition in the teaching classroom: A Review. EAI Endorsed Transactions on AI and Robotics. 2024;3.
17. Tang W. Review of Image Classification Algorithms Based on Graph Convolutional Networks. EAI Endorsed Transactions on AI and Robotics. 2023;2.
18. Goyal B, Dogra A, Agrawal S, Sohi BS, Sharma A. Image denoising review: From classical to state-of-the-art approaches. Information fusion. 2020; 55:220–44.
19. Bodavarapu P, Srinivas P. Facial expression recognition for low resolution images using convolutional neural networks and denoising techniques. Indian J Sci Technol. 2021; 14:971–83.
20. Boussaad L, Boucetta A. Deep-learning based descriptors in application to aging problem in face recognition. Journal of King Saud University-Computer and Information Sciences. 2022;34(6):2975–81.
21. Cengiz E, Kelek MM, Oğuz Y, Yılmaz C. Classification of breast cancer with deep learning from noisy images using wavelet transform. Biomedical Engineering/Biomedizinische Technik. 2022;67(2):143–50.
22. Chakraborty S, Shaikh SH, Chakrabarti A, Ghosh R. An image denoising technique using quantum wavelet transform. International Journal of Theoretical Physics. 2020; 59:3348–71.
23. Dharini S, Jain S. An efficient and hybrid pulse coupled neural network-based object detection framework based on machine learning. Computers & Electrical Engineering. 2021; 96:107615.
24. Fan L, Zhang F, Fan H, Zhang C. Brief review of image denoising techniques. Vis Comput Ind Biomed Art. 2019;2(1):7.
25. Fuad MTH, Fime AA, Sikder D, Iftee MAR, Rabbi J, Al-Rakhami MS, et al. Recent advances in deep learning techniques for face recognition. IEEE Access. 2021; 9:99112–42.
26. Ghose S, Singh N, Singh P. Image denoising using deep learning: Convolutional neural network. In: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE; 2020. p. 511–7.
27. Gopatoti A, Gopathoti KK, Shanganthi SP, Nirmala C. Image denoising using spatial filters and image transforms: A review. International Journal for Research in Applied Science & Engineering Technology (IJRASET). 2018.
28. Gu S, Timofte R. A brief review of image denoising algorithms and beyond. Inpainting and Denoising Challenges. 2019;1–21.
29. Ilesanmi AE, Ilesanmi TO. Methods for image denoising using convolutional neural network: a review. Complex & Intelligent Systems. 2021;7(5):2179–98.
30. Ketab F, Russel NS, Selvaraj A, Buhari SM. Parallel deep learning architecture with customized and learnable filters for low-resolution face recognition. Vis Comput. 2023;39(12):6699–710.
31. Kim J, Klegrewe M, Unger W. Gauge Corrections to Strong Coupling Lattice QCD on Anisotropic Lattices. arXiv preprint arXiv:200106797. 2020.
32. Lefkimmiatis S. Universal denoising networks: a novel CNN architecture for image denoising. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. p. 3204–13.
33. Li M, Huang B, Tian G. A comprehensive survey on 3D face recognition methods. Eng Appl Artif Intell. 2022; 110:104669.
34. Liang H, Gao J, Qiang N. A novel framework based on wavelet transform and principal component for face recognition under varying illumination. Applied Intelligence. 2021; 51:1762–83.
35. Liu D, Wen B, Jiao J, Liu X, Wang Z, Huang TS. Connecting image denoising and high-level vision tasks via deep learning. IEEE Transactions on Image Processing. 2020; 29:3695–706.
36. Lu Y, Khan M, Ansari MD. Face recognition algorithm based on stack denoising and self-encoding LBP. Journal of Intelligent Systems. 2022;31(1):501–10.
37. Manganelli Conforti P, D’Acunto M, Russo P. Deep learning for chondrogenic tumor classification through wavelet transform of Raman spectra. Sensors. 2022;22(19):7492.
38. Mei F, Qian D, Nie Y, Wang B, Liang A, Li H. Biomedical Applications of Wavelet Transform Algorithm on Deep Learning Ultrasonic Image Optimization as a Prognosis Model for Acute Myocarditis. J Biomed Nanotechnol. 2024;20(3):604–13.
39. Mohammed SA, Abdulrahman AA, Tahir FS. Emotions students’ faces recognition using hybrid deep learning and discrete chebyshev wavelet transformations. Int J Math Comput Sci. 2022;17(3):1405–17.
40. Mustaqim T, Tsaniya H, Adhiyaksa FA, Suciati N. Wavelet transformation and local binary pattern for data augmentation in deep learning-based face recognition. In: 2022 10th International Conference on Information and Communication Technology (ICoICT). IEEE; 2022. p. 362–7.
41. Onur TÖ. Improved image denoising using wavelet edge detection based on Otsu’s thresholding. Acta Polytechnica Hungarica. 2022;19(2):79–92.
42. Pang T, Zheng H, Quan Y, Ji H. Recorrupted-to-recorrupted: unsupervised deep learning for image denoising. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021. p. 2043–52.
43. Paul A, Kundu A, Chaki N, Dutta D, Jha CS. Wavelet enabled convolutional autoencoder based deep neural network for hyperspectral image denoising. Multimed Tools Appl. 2022;1–27.
44. Shukla A, Seethalakshmi K, Hema P, Musale JC. An Effective Approach for Image Denoising Using Wavelet Transform Involving Deep Learning Techniques. In: 2023 4th International Conference on Smart Electronics and Communication (ICOSEC). IEEE; 2023. p. 1381–6.
45. Wang J, Hammer F, Yang Y, Pawlowski MS, Mamon GA, Wang H. The accretion history of the Milky Way: III. Hydrodynamical simulations of Galactic dwarf galaxies at first infall. Mon Not R Astron Soc. 2024;527(3):7144–57.
46. Guhathakurta R. Denoising of image: A wavelet based approach. In: 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON). IEEE; 2017. p. 194–7.
47. Suresh K V. An improved image denoising using wavelet transform. In: 2015 International Conference on Trends in Automation, Communications and Computing Technology (I-TACT-15). IEEE; 2015. p. 1–5.
48. Tian C, Xu Y, Zuo W. Image denoising using deep CNN with batch renormalization. Neural Networks. 2020; 121:461–73.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2024 Hewa Majeed Zangana, Firas Mahmood Mustafa
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.