Three robust edges stopping functions for image denoising
DOI:
https://doi.org/10.5269/bspm.45945Resumo
In this paper, we present three strong edge stopping functions for image enhancement. These edge stopping functions have the advantage of effectively removing the image noise while preserving the true edges and other important features. The obtained results show an improved quality for the restored images compared to existing restoration models.
Referências
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12. Chen, Shifeng and Liu, Ming and Zhang, Wei and Liu, Jianzhuang; Edge preserving image denoising with a closed form solution, Pattern Recognition, 46, No.3, 976-988, (2013). https://doi.org/10.1016/j.patcog.2012.08.014
13. Barbu, Tudor; Robust anisotropic diffusion scheme for image noise removal, Procedia Computer Science, 35, No.3, 522-530, (2014). https://doi.org/10.1016/j.procs.2014.08.133
14. Barbu, Tudor and Morosanu, Costica; Image Restoration using a Nonlinear Second-order Parabolic PDE-based Scheme, Analele Universitatii" Ovidius" Constanta-Seria Matematica, 25, No.1, 48-33, (2017). https://doi.org/10.1515/auom-2017-0003
15. Kamalaveni, V and Rajalakshmi, R Anitha and Narayanankutty, K. A.; Image denoising using variations of PeronaMalik model with different edge stopping functions, Procedia Computer Science, 58, 673-682, (2015). https://doi.org/10.1016/j.procs.2015.08.087
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17. Wang, Y Q and Guo, Jichang and Chen, Wufan and Zhang, Wenxue; Image denoising using modified Perona-Malik model based on directional Laplacian, Signal Processing, 93, No.9, 2548-2558, (2013). https://doi.org/10.1016/j.sigpro.2013.02.020
18. Wang, Zhou and Bovik, Alan C and Sheikh, Hamid R and Simoncelli, Eero P.; Image quality assessment: from error visibility to structural similarity, IEEE transactions on image processing, 13, No.4, 600-612, (2004). https://doi.org/10.1109/TIP.2003.819861
19. Guo, Zhichang and Sun, Jiebao and Zhang, Dazhi and Wu, Boying; Adaptive Perona-Malik model based on the variable exponent for image denoising, IEEE Transactions on Image Processing, 21, No.3, 958-967, (2012). https://doi.org/10.1109/TIP.2011.2169272
2. Tian, Haiying and Cai, Hongmin and Lai, Jian-Huang and Xu, Xiaoyin; Effective image noise removal based on difference eigenvalueImage Processing (ICIP), 2011 18th IEEE International Conference on, 3357-3360, (2011). https://doi.org/10.1109/ICIP.2011.6116392
3. Monteil, Jerome and Beghdadi, Azeddine; A new interpretation and improvement of the nonlinear anisotropic diffusion for image enhancementIEEE Transactions on Pattern Analysis and Machine Intelligence, 21, No.9, 940-946, (1999). https://doi.org/10.1109/34.790435
4. Weickert, Joachim; Coherence-enhancing diffusion filtering,International journal of computer vision, 31,No.2-3, 111-127, (1999). https://doi.org/10.1023/A:1008009714131
5. Yuan, Jianjun and Wang, Jianjun; Perona-malik model with a new diffusion coefficient for image denoising, International Journal of Image and Graphics, 16,No.2, 1650011, (2016). https://doi.org/10.1142/S021946781650011X
6. Liu, Kui and Tan, Jieqing and Su, Benyue; Adaptive anisotropic diffusion for image denoising based on structure tensor, Digital Home (ICDH), 2014 5th International Conference on, 111-116, (2014). https://doi.org/10.1109/ICDH.2014.29
7. Rudin, Leonid I and Osher, Stanley and Fatemi, Emad; Nonlinear total variation based noise removal algorithms, Physica D: nonlinear phenomena, 60, No.1-4, 259-268, (1992). https://doi.org/10.1016/0167-2789(92)90242-F
8. Black, Michael J and Sapiro, Guillermo and Marimont, David H and Heeger, David; Robust anisotropic diffusion, IEEE Transactions on image processing, 7, No.3, 421-432, (1998). https://doi.org/10.1109/83.661192
9. Perona, Pietro and Malik, Jitendra; Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on pattern analysis and machine intelligence, 12, No.7, 629-639, (1990). https://doi.org/10.1109/34.56205
10. Whitaker, Ross T and Pizer, Stephen, M.; A multi-scale approach to nonuniform diffusion, CVGIP Image Understanding, 57, 99-99, (1993). https://doi.org/10.1006/ciun.1993.1006
11. Osher, Stanley and Rudin, Leonid, I.; Feature-oriented image enhancement using shock filters, SIAM Journal on numerical analysis, 27, No.4, 919-940, (1990). https://doi.org/10.1137/0727053
12. Chen, Shifeng and Liu, Ming and Zhang, Wei and Liu, Jianzhuang; Edge preserving image denoising with a closed form solution, Pattern Recognition, 46, No.3, 976-988, (2013). https://doi.org/10.1016/j.patcog.2012.08.014
13. Barbu, Tudor; Robust anisotropic diffusion scheme for image noise removal, Procedia Computer Science, 35, No.3, 522-530, (2014). https://doi.org/10.1016/j.procs.2014.08.133
14. Barbu, Tudor and Morosanu, Costica; Image Restoration using a Nonlinear Second-order Parabolic PDE-based Scheme, Analele Universitatii" Ovidius" Constanta-Seria Matematica, 25, No.1, 48-33, (2017). https://doi.org/10.1515/auom-2017-0003
15. Kamalaveni, V and Rajalakshmi, R Anitha and Narayanankutty, K. A.; Image denoising using variations of PeronaMalik model with different edge stopping functions, Procedia Computer Science, 58, 673-682, (2015). https://doi.org/10.1016/j.procs.2015.08.087
16. Li, Xiaoping and Chen, Tongwen; Nonlinear diffusion with multiple edginess thresholds, Pattern Recognition, 27, No.8, 1029-1037, (1994). https://doi.org/10.1016/0031-3203(94)90142-2
17. Wang, Y Q and Guo, Jichang and Chen, Wufan and Zhang, Wenxue; Image denoising using modified Perona-Malik model based on directional Laplacian, Signal Processing, 93, No.9, 2548-2558, (2013). https://doi.org/10.1016/j.sigpro.2013.02.020
18. Wang, Zhou and Bovik, Alan C and Sheikh, Hamid R and Simoncelli, Eero P.; Image quality assessment: from error visibility to structural similarity, IEEE transactions on image processing, 13, No.4, 600-612, (2004). https://doi.org/10.1109/TIP.2003.819861
19. Guo, Zhichang and Sun, Jiebao and Zhang, Dazhi and Wu, Boying; Adaptive Perona-Malik model based on the variable exponent for image denoising, IEEE Transactions on Image Processing, 21, No.3, 958-967, (2012). https://doi.org/10.1109/TIP.2011.2169272
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2021-12-20
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