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Structure extraction of images using anisotropic diffusion with directional second neighbour derivative operator

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Abstract

The aim of structure extraction is to decompose an image into prominent structures and textures. In this paper, we present a new structure extraction method which has two main steps. First, high-frequency components due to the texture information in the original image are alleviated by a pre-smoothing filter. The result is then processed by a new anisotropic diffusion algorithm which uses a second neighbour derivative (SND) operator instead of the first neighbour derivative operator. We have demonstrated that the SND operator is better suited for applications such as texture smoothing. We have also presented a detailed study of the proposed method including the selection of the pre-smoothing filter, the number of iterations, and the scale parameter in the anisotropic diffusion algorithm. We have conducted experiments to compare the performance of the proposed method with those state-of-the-art structure extraction algorithms in a wide range of image editing applications such as: superpixel segmentation, texture transfer, contrast enhancement, and pencil drawing. We show that while the running speed of the proposed method is the fastest, its performance is competitive to other methods.

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Notes

  1. One of the reviewers of this paper suggested this application.

References

  1. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282

    Article  Google Scholar 

  2. Al-nasrawi M, Deng G, Thai B (2018) Edge-aware smoothing through adaptive interpolation. SIViP 12(2):347–354

    Article  Google Scholar 

  3. Arnheim R (1956) Art and visual perception: a psychology of the creative eye. University of California Press, Berkeley & Los Angeles

    Google Scholar 

  4. Aujol JF, Gilboa G, Chan T, Osher S (2006) Structure-texture image decomposition—modeling, algorithms, and parameter selection. Int J Comput Vis 67 (1):111–136

    Article  Google Scholar 

  5. Bao L, Song Y, Yang Q, Yuan H, Wang G (2014) Tree filtering: efficient structure-preserving smoothing with a minimum spanning tree. IEEE Trans Image Process 23(2):555–569

    Article  MathSciNet  Google Scholar 

  6. Buades A, Le TM, Morel JM, Vese LA (2010) Fast cartoon + texture image filters. IEEE Trans Image Process 19(8):1978–1986

    Article  MathSciNet  Google Scholar 

  7. Cho H, Lee H, Kang H, Lee S (2014) Bilateral texture filtering. ACM Trans Graph 33(4):128:1–128:8

    Article  Google Scholar 

  8. Deng G (2016) Edge-aware bma filters. IEEE Trans Image Process 25(1):439–454

    Article  MathSciNet  Google Scholar 

  9. Du H, Jin X, Willis PJ (2016) Two-level joint local Laplacian texture filtering. Vis Comput 32(12):1537–1548

    Article  Google Scholar 

  10. Farbman Z, Fattal R, Lischinski D, Szeliski R (2008) Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans Graph 27 (3):67:1–10

    Article  Google Scholar 

  11. Gastal ESL, Oliveira MM (2011) Domain transform for edge-aware image and video processing. ACM Trans Graph 30(4):69:1–69:12

    Article  Google Scholar 

  12. Ham B, Cho M, Ponce J (2015) Robust image filtering using joint static and dynamic guidance. In: Proc IEEE conference on computer vision and pattern recognition, pp 4823–4831

  13. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  14. Jeon J, Lee H, Kang H, Lee S (2016) Scale-aware structure-preserving texture filtering. In: Computer graphics forum, vol 35, pp 77–86

    Article  Google Scholar 

  15. Jiang X, Yao H, Liu S (2017) How many zero crossings? a method for structure-texture image decomposition. Comput Graph 68:129–141

    Article  Google Scholar 

  16. Karacan L, Erdem E, Erdem A (2013) Structure-preserving image smoothing via region covariances. ACM Trans Graph 32(6):176:1–176:11

    Article  Google Scholar 

  17. Kopf J, Cohen MF, Lischinski D, Uyttendaele M (2007) Joint bilateral upsampling. ACM Trans Graph 26(3):96–99

    Article  Google Scholar 

  18. Lan X, Ma AJ, Yuen PC (2014) Multi-cue visual tracking using robust feature-level fusion based on joint sparse representation. In: Proc IEEE conference on computer vision and pattern recognition, pp 1194–1201

  19. Lan X, Ma AJ, Yuen PC, Chellappa R (2015) Joint sparse representation and robust feature-level fusion for multi-cue visual tracking. IEEE Trans Image Process 24(12):5826–5841

    Article  MathSciNet  Google Scholar 

  20. Lan X, Yuen PC, Chellappa R (2017) Robust mil-based feature template learning for object tracking. In: Proc conference on artificial intelligence (AAAI), pp 4118–4125

  21. Lan X, Zhang S, Yuen PC (2016) Robust joint discriminative feature learning for visual tracking. In: Proc international joint conference on artificial intelligence, pp 3403–3410

  22. Lan X, Zhang S, Yuen PC, Chellappa R (2018) Learning common and feature-specific patterns: a novel multiple-sparse-representation-based tracker. IEEE Trans Image Process 27(4):2022–2037

    Article  MathSciNet  Google Scholar 

  23. Lee H, Jeon J, Kim J, Lee S (2017) Structure-texture decomposition of images with interval gradient. In: Computer graphics forum, vol 36, pp 262–274

    Article  Google Scholar 

  24. Li Y, Guo F, Tan RT, Brown MS (2014) A contrast enhancement framework with jpeg artifacts suppression. In: European conference on computer vision, Springer, pp 174–188

  25. Lindeberg T (1994) Scale-space theory: a basic tool for analyzing structures at different scales. J Appl Stat 21(1-2):225–270

    Article  Google Scholar 

  26. Lu C, Xu L, Jia J (2012) Combining sketch and tone for pencil drawing production. In: Proc symposium on non-photorealistic animation and rendering, pp 65–73

  27. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639

    Article  Google Scholar 

  28. Petschnigg G, Szeliski R, Agrawala M, Cohen M, Hoppe H, Toyama K (2004) Digital photography with flash and no-flash image pairs. ACM Trans Graph 23(3):664–672

    Article  Google Scholar 

  29. Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D, Nonlinear Phenomena 60(1-4):259–268

    Article  MathSciNet  Google Scholar 

  30. Su Z, Luo X, Deng Z, Liang Y, Ji Z (2013) Edge-preserving texture suppression filter based on joint filtering schemes. IEEE Trans Multimedia 15(3):535–548

    Article  Google Scholar 

  31. Su Z, Zeng B, Miao J, Luo X, Yin B, Chen Q (2017) Relative reductive structure-aware regression filter. J Comput Appl Math 329:244–255

    Article  MathSciNet  Google Scholar 

  32. Subr K, Soler C, Durand F (2009) Edge-preserving multiscale image decomposition based on local extrema. ACM Trans Graph 28(5):147:1–147:9

    Article  Google Scholar 

  33. Thai B, Al-nasrawi M, Deng G, Su Z (2017) Semi-guided bilateral filter. IET Image Process 11(7):512–521

    Article  Google Scholar 

  34. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: 6Th international conference on computer vision, pp 839–846

  35. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13 (4):600–612

    Article  Google Scholar 

  36. Wu H, Xu D, Yuan G (2017) Region covariance based total variation optimization for structure-texture decomposition. Multimedia Tools and Applications

  37. Xu L, Lu C, Xu Y, Jia J (2011) Image smoothing via L 0, gradient minimization. ACM Trans Graph 30(6):174:1–174:12

    Google Scholar 

  38. Xu L, Yan Q, Xia Y, Jia J (2012) Structure extraction from texture via relative total variation. ACM Trans Graph 31(6):139

    Google Scholar 

  39. Yin W, Goldfarb D, Osher S (2005) Image cartoon-texture decomposition and feature selection using the total variation regularized l1 functional. In: Variational, geometric, and level set methods in computer vision, Springer, pp 73–84

  40. Zhang Q, Shen X, Xu L, Jia J (2014) Rolling guidance filter. In: European conference on computer vision, Springer, pp 815–830

  41. Zhou Z, Wang B, Ma J (2018) Scale-aware edge-preserving image filtering via iterative global optimization. IEEE Trans Multimedia 20(6):1392–1405

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Engineering, La Trobe University, Bundoora, Victoria, 3086, Australia

    Mukhalad Al-nasrawi, Guang Deng & Waseem Waheed

  2. Al-Musaib Technical College, Al-Furat Al-Awsat Technical University, Babylon, 51009, Iraq

    Mukhalad Al-nasrawi

Authors
  1. Mukhalad Al-nasrawi
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  2. Guang Deng
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  3. Waseem Waheed
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Correspondence to Mukhalad Al-nasrawi.

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Al-nasrawi, M., Deng, G. & Waheed, W. Structure extraction of images using anisotropic diffusion with directional second neighbour derivative operator. Multimed Tools Appl 78, 6385–6407 (2019). https://doi.org/10.1007/s11042-018-6377-7

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  • DOI: https://doi.org/10.1007/s11042-018-6377-7

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