Image Denoising using Adaptive Patch Clustering with Suboptimal Wiener Filter in PCA Domain

Image Denoising using Adaptive Patch Clustering with Suboptimal Wiener Filter in PCA Domain

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-11
Year of Publication : 2022
Authors : Swati Rane, Lakshmappa Ragha, Siddalingappagouda Biradar, Vaibhav Pandit
DOI : 10.14445/22315381/IJETT-V70I11P203

How to Cite?

Swati Rane, Lakshmappa Ragha, Siddalingappagouda Biradar, Vaibhav Pandit, "Image Denoising using Adaptive Patch Clustering with Suboptimal Wiener Filter in PCA Domain," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 19-27, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P203

Abstract
The degradation in the visual quality of images is often seen due to various noises added inevitably at the time of image acquisition. Its restoration has thus become a fundamental and significant problem in image processing. Many attempts have been made in the recent past to efficiently denoise such images. But, the best possible solution to this problem is still an open research problem. This paper validates the effectiveness of one such popular image denoising approach, where an adaptive image patch clustering is followed by the suboptimal Wiener filter operation in the Principal Component Analysis (PCA) domain. The experimentation is conducted on grayscale images corrupted by four different noise types: speckle, salt & pepper, Gaussian, and Poisson. The efficiency of image denoising is quantified in terms of various famous image quality metrics. The comprehensive performance analysis of the denoising approach against the four noise models underlies its suitability for various applications. It certainly gives the new researchers a direction for selecting the image-denoising method.

Keywords
Adaptive clustering, Image denoising, Principal component analysis, Wiener filter.

Reference
[1] Jiří Borovec, Jan Kybic, Ignacio Arganda-Carreras, Dmitry V. Sorokin, Gloria Bueno, Alexander V. Khvostikov, Spyridon Bakas, Eric, I-Chao Chang, Stefan Heldmann, Kimmo Kartasalo, Leena Latonen, Johannes Lotz, Michelle Noga, Sarthak Pati, Kumaradevan Punithakumar, Pekka Ruusuvuori, Andrzej Skalski, Nazanin Tahmasebi, Masi Valkonen, Ludovic Venet, Yizhe Wang, Nick Weiss, Marek Wodzinski, Yu Xiang, Yan Xu, Yan Yan, Paul Yushkevich, Shengyu Zhao, and Arrate Muñoz-Barrutia, “ANHIR: Automatic Non-Rigid Histological Image Registration Challenge,” IEEE Transactions on Medical Imaging, vol. 39, no. 10, pp. 3042–3052, 2020. Crossref, http://doi.org/10.1109/TMI.2020.2986331
[2] M. Li, Y. Chen, Z. Ji, K. Xie, S. Yuan, Q. Chen, and S. Li, "Image Projection Network: 3D to 2D Image Segmentation in Octa Images," IEEE Transactions on Medical Imaging, vol. 39, no. 11, pp. 3343–3354, 2020. Crossref, http://doi.org/10.1109/TMI.2020.2992244
[3] Y. Pei, Y. Huang, Q. Zou, X. Zhang, and S. Wang, "Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 4, pp. 1239–1253, 2021. Crossref, http://doi.org/10.1109/TPAMI.2019.2950923
[4] P. K. Mishro, S. Agrawal, R. Panda, and A. Abraham, "A Survey on State of-the-Art Denoising Techniques for Brain Magnetic Resonance Images," IEEE Reviews in Biomedical Engineering, vol. 15, pp. 184–199, 2022. Crossref, http://doi.org/10.1109/RBME.2021.3055556
[5] M. H. Alkinani and M. R. El-Sakka, "Patch-Based Models and Algorithms for Image Denoising: A Comparative Review Between Patch-Based Images Denoising Methods for Additive Noise Reduction," EURASIP Journal on Image and Video Processing, vol. 58, pp. 1–27, 2017. Crossref, https://doi.org/10.1186/s13640-017-0203-4
[6] K.Manivel, and Dr. R.Samson Ravindran, "A Comparative Study of Impulse Noise Reduction in Digital Images for Classical and Fuzzy Filters," International Journal of Engineering Trends and Technology, vol. 4, no. 10, pp. 4584-4589, 2013.
[7] Y. Vishnu Tej, M. James Stephen, PVGD. Prasad Reddy, and Praveen Choppala, "A Novel Methodology for Denoising Impulse Noise in Satellite Images through Isolated Vector Median Filter with k-means Clustering," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 272-283, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P229
[8] R. James, A. M. Jolly, C. Anjali, and D. Michael, "Image Denoising using Adaptive PCA and SVD," in 2015 Fifth International Conference on Advances in Computing and Communications (ICACC), pp. 383–386, 2015. Crossref, https://doi.org/10.1109/ICACC.2015.82.
[9] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image Denoising with Block-Matching and 3D Filtering," in Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 2006. Crossref, https://doi.org/10.1117/12.643267
[10] A. A. Yahya, J. Tan, B. Su, M. Hu, Y. Wang, K. Liu, and A. N. Hadi, "BM3D Image Denoising Algorithm Based on an Adaptive Filtering," Multimedia Tools and Applications, Springer, vol. 79, pp. 20391–20427, 2020. Crossref, https://doi.org/10.1007/s11042-020-08815-8
[11] J. Gao and Q. Wang, "BM3D Image Denoising Algorithm Based on K-Means Clustering," in Advanced Graphic Communications and Media Technologies, vol. 417, pp. 265-272, 2016. Crossref, https://doi.org/10.1007/978-981-10-3530-2_33
[12] L. Zhang, W. Dong, D. Zhang, and G. Shi, "Two-Stage Image Denoising by Principal Component Analysis with Local Pixel Grouping," Pattern Recognition, vol. 43, no. 4, pp. 1531–1549, 2010. Crossref, https://doi.org/10.1016/j.patcog.2009.09.023
[13] S. Routray, A. K. Ray, and C. Mishra, "An Efficient Image Denoising Method Based on Principal Component Analysis with Learned Patch Groups," Signal, Image and Video Processing, Springer, vol. 13, pp. 1405–1412, 2019. Crossref, https://doi.org/10.1007/s11760-019-01489-2
[14] F. Jing, H. Shaohai, and M. Xiaole, "SAR Image De-Noising Via Groupingbased PCA and Guided Filter," Journal of Systems Engineering and Electronics, vol. 32, no. 1, pp. 81–91, 2021. Crossref, https://doi.org/10.23919/JSEE.2021.000009
[15] D. Muresan and T. Parks, "Adaptive Principal Components and Image Denoising," in Proceedings 2003 International Conference on Image Processing (Cat. No. 03CH37429), vol. 1, pp. I–101, 2003. Crossref, https://doi.org/10.1109/ICIP.2003.1246908
[16] L. Xu, J. Li, Y. Shu, and J. Peng, "SAR Image Denoising Via Clusteringbased Principal Component Analysis," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 11, pp. 6858–6869, 2014. Crossref, https://doi.org/10.1109/TGRS.2014.2304298
[17] Y. Lin, R. Hardie, and K. Barner, "Subspace Partition Weighted Sum Filters for Image Restoration," IEEE Signal Processing Letters, vol. 12, no. 9, pp. 613–616, 2005. Crossref, https://doi.org/10.1109/LSP.2005.853052
[18] P. Chatterjee and P. Milanfar, "Patch-Based Near-Optimal Image Denoising," IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1635–1649, 2012. Crossref, https://doi.org/10.1109/TIP.2011.2172799
[19] S. Suresh, S. Lal, C. Chen, and T. Celik, "Multispectral Satellite Image Denoising Via Adaptive Cuckoo Search-Based Wiener Filter," IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 8, pp. 4334–4345, 2018. Crossref, https://doi.org/10.1109/TGRS.2018.2815281
[20] Leelavathi H P and Dr. J Prakash, "Effective Speckle Noise Removal of SAR Image Based on Combination of Modified PCA and HMF with Enhancement," International Journal of Engineering Trends and Technology, vol. 61, no. 3, pp. 171-177, 2018. Crossref, https://doi.org/10.14445/22315381/IJETT-V61P228
[21] P. Chatterjee and P. Milanfar, “Is Denoising Dead?” IEEE Transactions on Image Processing, vol. 19, no. 4, pp. 895–911, 2010. Crossref, https://doi.org/10.1109/TIP.2009.2037087
[22] P. Chatterjee and P. Milanfar, "Patch-Based Near-Optimal Image Denoising," IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1635–1649, 2012. Crossref, https://doi.org/10.1109/TIP.2011.2172799
[23] M. Cao, S. Li, R. Wang, and N. Li, "Interferometric Phase Denoising by Median Patch-Based Locally Optimal Wiener Filter," IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 8, pp. 1730–1734, 2015. Crossref, https://doi.org/10.1109/LGRS.2015.2422788
[24] Rabiya Banu A, and Kannan R, "Quantitative and Qualitative Analysis for Lung Nodule Segmentation," SSRG International Journal of Electronics and Communication Engineering, vol. 6, no. 5, pp. 16-21, 2019. Crossref, https://doi.org/10.14445/23488549/IJECE-V6I5P104
[25] R. C. Gonzalez and R. E. Woods, "Digital Image Processing," 3rd Ed., UpperSaddle River, NJ: Prentice Hall, 2008.
[26] A. K. Boyat and B. K. Joshi, "A Review Paper: Noise Models in Digital Image Processing," ArXiv, vol. abs/1505.03489, 2015. Crossref, https://doi.org/10.48550/arXiv.1505.03489
[27] J. Bigot, C. Deledalle, and D. F'eral, "Generalized Sure for Optimal Shrinkage of Singular Values in Low-Rank Matrix Denoising," Journal of Machine Learning Research, vol. 18, no. 137, pp. 1–50, 2017.
[28] J. Lerga, M. Vrankic, and V. Sucic, "A Signal Denoising Method Based on the Improved ICI Rule," IEEE Signal Processing Letters, vol. 15, pp. 601–604, 2008. Crossref, https://doi.org/10.1109/LSP.2008.2001817.
[29] L. Zhang, W. Dong, D. Zhang, and G. Shi, "Two-Stage Image Denoising by Principal Component Analysis with Local Pixel Grouping," Pattern Recognition, vol. 43, no. 4, pp. 1531–1549, 2010. Crossref, https://doi.org/10.1016/j.patcog.2009.09.023
[30] J. Chen, J. Benesty, Y. Huang, and S. Doclo, "New Insights Into the Noise Reduction Wiener Filter," IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 4, pp. 1218–1234, 2006. Crossref, https://doi.org/10.1109/TSA.2005.860851
[31] 2021. [Online]. Available: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
[32] U. Sara, M. Akter, and M. S. Uddin, "Image Quality Assessment Through FSIM, SSIM, MSE and PSNR—A Comparative Study," Journal of Computer and Communications, vol. 7, pp. 8–18, 2019. Crossref, https://doi.org/10.4236/jcc.2019.73002
[33] K. Ote, F. Hashimoto, A. Kakimoto, T. Isobe, T. Inubushi, R. Ota, A. Tokui, A. Saito, T. Moriya, T. Omura, E. Yoshikawa, A. Teramoto, and Y. Ouchi, "Kinetics-Induced Block Matching and 5-D Transform Domain Filtering for Dynamic Pet Image Denoising," IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 4, no. 6, pp. 720–728, 2020. Crossref, https://doi.org/10.1109/TRPMS.2020.3000221