DOI QR코드

DOI QR Code

A Modified Steering Kernel Filter for AWGN Removal based on Kernel Similarity

  • Cheon, Bong-Won (Department of Intelligent Robot Engineering, Pukyong National University) ;
  • Kim, Nam-Ho (School of Electrical Engineering, Pukyong National University)
  • 투고 : 2021.12.24
  • 심사 : 2022.08.24
  • 발행 : 2022.09.30

초록

Noise generated during image acquisition and transmission can negatively impact the results of image processing applications, and noise removal is typically a part of image preprocessing. Denoising techniques combined with nonlocal techniques have received significant attention in recent years, owing to the development of sophisticated hardware and image processing algorithms, much attention has been paid to; however, this approach is relatively poor for edge preservation of fine image details. To address this limitation, the current study combined a steering kernel technique with adaptive masks that can adjust the size according to the noise intensity of an image. The algorithm sets the steering weight based on a similarity comparison, allowing it to respond to edge components more effectively. The proposed algorithm was compared with existing denoising algorithms using quantitative evaluation and enlarged images. The proposed algorithm exhibited good general denoising performance and better performance in edge area processing than existing non-local techniques.

키워드

참고문헌

  1. T. K. Kim, I. H. Song, and S. H. Lee, "Noise reduction of HDR detail layer using a kalman filter adapted to local image activity," Journal of Korea Multimedia Society, vol. 22, no. 1, pp. 10-17, Jan. 2019. DOI: 10.9717/kmms.2019.22.1.010.
  2. B. W. Cheon and N. H. Kim, "Noise removal algorithm considering high frequency components in AWGN environments," Journal of the Korea Institute of Information and Communication Engineering, vol. 22, no. 6, pp. 867-873, Jun. 2018. DOI: 10.6109/jkiice.2018.22.6.867.
  3. A. Buades, B. Coll, and J. M. Morel, "Non-local means denoising," Image Processing On Line, vol. 1, pp. 208-212, Sep. 2011. DOI: 10.5201/ipol.2011.bcm_nlm.
  4. Z. Sun, B. Han, J. Li, J. Zhang, and X. Gao, "Weighted guided image filtering with steering kernel," IEEE Transactions on Image Processing, vol. 29, pp. 500-508, Jul. 2019. DOI: 10.1109/TIP.2019.2928631.
  5. A. Buades, B. Coll, and J. M. Morel, "A non-local algorithm for image denoising," in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego: CA, USA, pp. 1-6, 2005. DOI: 10.1109/CVPR.2005.38.
  6. M. Shi, T. Han, and S. Liu, "Total variation image restoration using hyper-laplacian prior with overlapping group sparsity," Signal Processing, vol. 126, pp. 65-76, Sep. 2016. DOI: 10.1016/j.sigpro.2015.11.022.
  7. C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," in Sixth International Conference on Computer Vision, Bombay, India, pp. 839-846, 1998. DOI: 10.1109/ICCV.1998.710815.
  8. L. I. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D: Nonlinear Phenomena, vol. 60, no. 1-4, pp. 259-268, Nov. 1992. DOI: 10.1016/0167-2789(92)90242-F.
  9. R. Lai, Y. Mo, Z. Liu, and J. Guan, "Local and nonlocal steering kernel weighted total variation model for image denoising," Symmetry, vol. 11, no. 3, pp. 1-16, Mar. 2019. DOI: 10.3390/sym11030329.
  10. H. Takeda, S. Farsiu, and P. Milanfar, "Kernel regression for image processing and reconstruction," IEEE Transactions on Image Processing, vol. 16, no. 2, pp. 349-366, Feb. 2007. DOI: 10.1109/TIP.2006.888330.
  11. K. Zhang, X. Gao, J. Li, and H. Xia, "Single image super-resolution using regularization of non-local steering kernel regression," Signal Processing, vol. 123, pp. 53-63, Jun. 2016. DOI: 10.1016/j.sigpro.2015.11.025.
  12. R. Chernyak, R. Mullakhmetov, V. Stepin, and S. Ikonin, "Block matching in noise suppression filter for video coding," in 2019 International Multi-Conference on Engineering, Computer and Information Sciences, Novosibirsk, Russia, pp. 187-190, 2019. DOI: 10.1109/SIBIRCON48586.2019.8958358.
  13. J. Immerkaer, "Fast noise variance estimation," Computer Vision and Image Understanding, vol. 64, no. 2, pp. 300-302, Sep. 1996. DOI: 10.1006/cviu.1996.0060.
  14. J. I. Shin, T. J. Kim, W. S. Yoon, and H. J. Park, "Improving satellite-aerial image matching success rate by image fusion," in 2018 2nd European Conference on Electrical Engineering and Computer Science, Bern, Switzerland, pp. 224-227, 2018. DOI: 10.1109/EECS.2018.00049.
  15. W. Changjie and N. Hua, "Algorithm of remote sensing image matching based on corner-point," in International Workshop on Remote Sensing with Intelligent Processing, Shanghai, China, pp. 1-4, 2017. DOI: 10.1109/RSIP.2017.7958803.