publication . Other literature type . Preprint . Article . 2019

Fast High-Dimensional Bilateral and Nonlocal Means Filtering

Pravin Nair; Kunal N. Chaudhury;
Open Access
  • Published: 01 Mar 2019
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Existing fast algorithms for bilateral and nonlocal means filtering mostly work with grayscale images. They cannot easily be extended to high-dimensional data such as color and hyperspectral images, patch-based data, flow-fields, etc. In this paper, we propose a fast algorithm for high-dimensional bilateral and nonlocal means filtering. Unlike existing approaches, where the focus is on approximating the data (using quantization) or the filter kernel (via analytic expansions), we locally approximate the kernel using weighted and shifted copies of a Gaussian, where the weights and shifts are inferred from the data. The algorithm emerging from the proposed approxim...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition
45 references, page 1 of 3

[1] P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629-639, 1990. [OpenAIRE]

[2] V. Aurich and J. Weule, “Non-linear gaussian filters performing edge preserving diffusion,” Mustererkennung, pp. 538-545, 1995. [OpenAIRE]

[3] S. M. Smith and J. M. Brady, “SUSAN- A new approach to low level image processing,” International Journal of Computer Vision, vol. 23, no. 1, pp. 45-78, 1997.

[4] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” Proc. IEEE International Conference on Computer Vision, pp. 839-846, 1998.

[5] D. Comaniciu and P. Meer, “Mean shift analysis and applications,” Proc. IEEE International Conference on Computer Vision, vol. 2, pp. 1197- 1203, 1999. [OpenAIRE]

[6] Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, “Edge-preserving decompositions for multi-scale tone and detail manipulation,” ACM Transactions on Graphics, vol. 27, no. 3, p. 67, 2008. [OpenAIRE]

[7] E. S. Gastal and M. M. Oliveira, “Domain transform for edge-aware image and video processing,” ACM Transactions on Graphics, vol. 30, no. 4, pp. 69:1-69:12, 2011.

[8] K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397- 1409, 2013.

[9] A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60-65, 2005.

[10] S. Paris, P. Kornprobst, J. Tumblin, and F. Durand, “Bilateral filtering: Theory and Applications,” Foundations and Trends R in Computer Graphics and Vision, vol. 4, no. 1, pp. 1-73, 2009.

[11] K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian et al., “Image denoising with block-matching and 3D filtering,” Proceedings of SPIE, vol. 6064, no. 30, pp. 606 414-606 414, 2006. [OpenAIRE]

[12] K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3142-3155, 2017.

[13] J. Darbon, A. Cunha, T. F. Chan, S. Osher, and G. J. Jensen, “Fast nonlocal filtering applied to electron cryomicroscopy,” Proc. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1331-1334, 2008. [OpenAIRE]

[14] E. S. Gastal and M. M. Oliveira, “Adaptive manifolds for real-time highdimensional filtering,” ACM Transactions on Graphics, vol. 31, no. 4, pp. 33:1-33:13, 2012.

[15] J. Wang, Y. Guo, Y. Ying, Y. Liu, and Q. Peng, “Fast non-local algorithm for image denoising,” Proc. IEEE International Conference on Image Processing, pp. 1429-1432, 2006.

45 references, page 1 of 3
Abstract
Existing fast algorithms for bilateral and nonlocal means filtering mostly work with grayscale images. They cannot easily be extended to high-dimensional data such as color and hyperspectral images, patch-based data, flow-fields, etc. In this paper, we propose a fast algorithm for high-dimensional bilateral and nonlocal means filtering. Unlike existing approaches, where the focus is on approximating the data (using quantization) or the filter kernel (via analytic expansions), we locally approximate the kernel using weighted and shifted copies of a Gaussian, where the weights and shifts are inferred from the data. The algorithm emerging from the proposed approxim...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition
45 references, page 1 of 3

[1] P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629-639, 1990. [OpenAIRE]

[2] V. Aurich and J. Weule, “Non-linear gaussian filters performing edge preserving diffusion,” Mustererkennung, pp. 538-545, 1995. [OpenAIRE]

[3] S. M. Smith and J. M. Brady, “SUSAN- A new approach to low level image processing,” International Journal of Computer Vision, vol. 23, no. 1, pp. 45-78, 1997.

[4] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” Proc. IEEE International Conference on Computer Vision, pp. 839-846, 1998.

[5] D. Comaniciu and P. Meer, “Mean shift analysis and applications,” Proc. IEEE International Conference on Computer Vision, vol. 2, pp. 1197- 1203, 1999. [OpenAIRE]

[6] Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, “Edge-preserving decompositions for multi-scale tone and detail manipulation,” ACM Transactions on Graphics, vol. 27, no. 3, p. 67, 2008. [OpenAIRE]

[7] E. S. Gastal and M. M. Oliveira, “Domain transform for edge-aware image and video processing,” ACM Transactions on Graphics, vol. 30, no. 4, pp. 69:1-69:12, 2011.

[8] K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397- 1409, 2013.

[9] A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60-65, 2005.

[10] S. Paris, P. Kornprobst, J. Tumblin, and F. Durand, “Bilateral filtering: Theory and Applications,” Foundations and Trends R in Computer Graphics and Vision, vol. 4, no. 1, pp. 1-73, 2009.

[11] K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian et al., “Image denoising with block-matching and 3D filtering,” Proceedings of SPIE, vol. 6064, no. 30, pp. 606 414-606 414, 2006. [OpenAIRE]

[12] K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3142-3155, 2017.

[13] J. Darbon, A. Cunha, T. F. Chan, S. Osher, and G. J. Jensen, “Fast nonlocal filtering applied to electron cryomicroscopy,” Proc. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1331-1334, 2008. [OpenAIRE]

[14] E. S. Gastal and M. M. Oliveira, “Adaptive manifolds for real-time highdimensional filtering,” ACM Transactions on Graphics, vol. 31, no. 4, pp. 33:1-33:13, 2012.

[15] J. Wang, Y. Guo, Y. Ying, Y. Liu, and Q. Peng, “Fast non-local algorithm for image denoising,” Proc. IEEE International Conference on Image Processing, pp. 1429-1432, 2006.

45 references, page 1 of 3
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