Feature Extraction for Patch Matching in Patch-Based Denoising Methods

Authors

  • Guangyi Chen Concordia University
  • Adam Krzyzak Concordia University

DOI:

https://doi.org/10.5566/ias.2812

Keywords:

Additive Gaussian white noise, image denoising, patch-based image denoising

Abstract

Patch-based image denoising is a popular topic in recent years. In existing works, the distance between two patches was calculated as their Euclidian distance. When the noise level is high, this approach may not be desirable in image denoising. In this paper, we propose to extract noise-robust feature vectors from image patches and match the image patches by their Euclidian distance of the feature vectors for grey scale image denoising. Our modification takes advantage of the fact that the mean of the Gaussian white noise is zero. For every patch in the noisy image, we use lines to divide the patch into two regions with equal area and we take the mean of the right region for each line. Hence, a number of features can be extracted. We use these extracted features to match the patches in the noisy image. By introducing feature-based patch matching, our method performs favourably for highly noisy images.

Author Biographies

Guangyi Chen, Concordia University

Guang Yi Chen holds a B.Sc. in Applied Mathematics, an M.Sc. in Computing Mathematics, an M.Sc. in Computer Science, and a Ph.D. in Computer Science. During his graduate and postdoctoral studies in Canada, he was awarded many prestigious fellowships. He has published over sixty-five scientific journal papers in his fields and holds two granted USA patents in image processing. He is currently affiliated to the Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada. He is the world's top 2% scientist ranked by Stanford University. His research interests include pattern recognition, image processing, machine learning, artificial intelligence, remote sensing, and scientific computing.

Adam Krzyzak, Concordia University

Adam Krzyzak received the M.Sc. and Ph.D. degrees in computer engineering from the Wrocław University of Science and Technology, Poland, in 1977 and 1980, respectively, and D.Sc. degree (habilitation) in computer engineering from the Warsaw University of Technology, Poland in 1998. In 2003 he received the Title of Professor from the President of the Republic of Poland. Since 1983, he has been with the Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada, where he is currently a full professor. He published over 350 papers on neural networks, pattern recognition, nonparametric estimation, image processing, computer vision and control. He has been an associate editor of IEEE Transactions on Neural Networks and IEEE Transactions on Information Theory and is presently an Associate Editor-in-Chief of the Pattern Recognition Journal. He was co-editor of the book Computer Vision and Pattern Recognition (Singapore: World Scientific, 1989) and is a co-author of the book A Distribution-Free Theory of Nonparametric Regression, New York: Springer, 2002. He is a Fellow of the IEEE and a Fellow of IAPR.

 

References

Antoni Buades (2005), A non-local algorithm for image denoising, Computer Vision and Pattern Recognition, 2:60-65.

Chatterjee, P. and Milanfar, P. (2012), Patch-based near-optimal image denoising, IEEE T Image Process, 21:1635-49.

Chen, G. Y., Bui, T. D. and Krzyzak, A. (2005a), Image denoising using neighbouring wavelet coefficients, Integr Comput-Aid E, 12:99-107.

Chen, G. Y., Bui, T. D. and Krzyzak (2005b), A. Image denoising with neighbour dependency and customized wavelet and threshold, Pattern Recogn, 38:115-24.

Chen, G. Y. and Kegl, B. (2007), Image denoising with complex ridgelets, Pattern Recogn, 40:578-85.

Chen, G. Y., Xie, W. F. and Dai, S. (2014), Images denoising with feature extraction for patch matching in block matching and 3D filtering, Proceedings of the Tenth International Conference on Intelligent Computing (ICIC), Taiyuan, China.

Chen, Q. and Wu, D. (2010), Image denoising by bounded block matching and 3D filtering, Signal Process, 90:2778-83.

Cho, D. and Bui, T. D. (2005), Multivariate statistical modeling for image denoising using wavelet transforms, Signal Process-Image, 20:77-89.

Cho, D., Bui, T. D. and Chen, G. Y. (2009), Image denoising based on wavelet shrinkage using neighbour and level dependency, Intl J Wavelets, Multi, 7:299-311.

Dabov, K., Foi, A., Katkovnik, V. and Egiazarian, K. (2007). Image denoising by sparse 3D transform-domain collaborative filtering, IEEE T Image Process, 16:2080-95.

Donoho, D. L. and Johnstone, I. M. (1994), Ideal spatial adaptation by wavelet shrinkage, Biometrika, 81:425-55.

Fathi, A. and Naghsh-Nilchi, A. R. (2012), Efficient image denoising method based on a new adaptive wavelet packet thresholding function, IEEE T Image Process, 21:3981-90.

Hou, Y., Zhao, C., Yang, D. and Cheng, Y. (2011), Comments on "Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering", IEEE T Image Process, 20:268-70.

Huang, T. S., Yang, G. J. and Tang, G. Y. (1979), A fast two-dimensional median filtering algorithm, IEEE T Acoust Speech, 27:13-8.

Kervrann, C. and Boulanger, J. (2006), Optimal spatial adaptation for patch-based image denoising, IEEE T Image Process, vol. 15:2866-78.

Kingsbury, N. G. (2001), Complex wavelets for shift invariant analysis and filtering of signals,

Journal of Applied and Computational Harmonic Analysis, 10:234-53.

Lebrun, M. (2012), An Analysis and Implementation of the BM3D Image Denoising Method, Image Processing On Line. http://dx.doi.org/10.5201/ipol.2012.l-bm3d.

Luisier, F., Blu, T. and Unser, M. (2007), A, new SURE approach to image denoising: Interscale orthogonal wavelet thresholding, IEEE T Image Process, 16:593-606.

Miller, M. and Kingsburg, N. (2008), Image denoising using derotated complex wavelet coefficients, IEEE T Image Process, 17:1500-11.

Motta, G., Ordentlich, E., Ramirez, I., Seroussi, G. and Weinberger, M. J. (2011), The iDUDE framework for grayscale image denoising, IEEE T Image Process, 20:1-21.

Rajwade, A., Rangarajan. A. and Banerjee, A. (2013), Image denoising using the higher order singular value decomposition, IEEE T Pattern Anal, 35:849-62.

Sendur, L. and Selesnick, I, W. (2002). Bivariate shrinkage with local variance estimation, IEEE Signal Proc Let, 9:438-41.

Talebi, H. and Milanfar, P. (2014) Global image denoising, IEEE T Image Process, 23:755-68.

Yue, H., Sun, X., Yang, J. and Wu, F. (2015), Image Denoising by Exploring External and Internal Correlations, IEEE T Image Process, 24:1967-82.

Zhang, K., Zuo, W., Chen, Y., Meng, D. and Zhang, L. (2017), Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE T Image Process, 26:3142-55.

Downloads

Published

2022-11-30

How to Cite

Chen, G., & Krzyzak, A. (2022). Feature Extraction for Patch Matching in Patch-Based Denoising Methods. Image Analysis and Stereology, 41(3), 217–227. https://doi.org/10.5566/ias.2812

Issue

Vol. 41 No. 3 (2022)

Section

Original Research Paper

License

Copyright (c) 2022 Guangyi Chen, Adam Krzyzak

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.