Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Adaptive fast local Laplacian filters and its edge-aware application

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

We present a new approach for edge-aware image processing, inspired by the principle of local Laplacian filters and fast local Laplacian filters. In contrast to the previous methods that primarily rely on fixed intensity threshold, our method adopts an adaptive parameter selection strategy in different regions of the processing image. This adaptive parameter selection strategy allows different intensity thresholds and different amplitude magnification factors in different pixels, moreover, a different remapping functions are adopted to process each pixel. At the same time, we propose an efficient and flexible method for obtaining the representation of image local variation, and based on the representation to select local Laplacian filters parameters adaptively. Our experiments shows that high-quality results in the detail enhancement and detail smoothing can be produced by our methods.

This is a preview of subscription content,log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Arnheim R (1956) Art and visual perception: A psychology of the creative eye, vol 16.3. University of California Press, Oakland, p 425

    Google Scholar 

  2. Aubert G, Kornprobst P (2006) Mathematical problems in image processing: partial differential equations and the calculus of variations, vol 147. Springer Science & Business Media, New York

    MATH  Google Scholar 

  3. Aubry M, Paris S, Hasinoff SW, Kautz J, Durand F (2014) Fast local laplacian filters: theory and applications. ACM Trans Graph (TOG) 33(5):167

    Article  Google Scholar 

  4. Aurich V, Weule J (1995) Non-linear gaussian filters performing edge preserving diffusion. In: Mustererkennung 1995. Springer, Berlin, pp 538–545

  5. Barash D (2002) Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation. IEEE Trans Pattern Anal Mach Intell 24(6):844–847

    Article  Google Scholar 

  6. Burt P, Adelson E (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540

    Article  Google Scholar 

  7. Chen J, Paris S, Durand F (2007) Real-time edge-aware image processing with the bilateral grid. ACM Trans Graph (TOG) 26(3):103

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Fattal R (2009) Edge-avoiding wavelets and their applications. ACM Trans Graph (TOG) 28(3):22

    Article  Google Scholar 

  11. Fattal R, Agrawala M, Rusinkiewicz S (2007) Multiscale shape and detail enhancement from multi-light image collections. ACM Trans Graph 26(3):51

    Article  Google Scholar 

  12. Gao Z, Zhang H, Xu GP et al (2015) Multi-perspective and multi-modality joint representation and recognition model for 3D action recognition[J]. Neurocomputing 151:554–564

    Article  Google Scholar 

  13. Gao Z, Zhang H, Xu GP et al (2015) Multi-view discriminative and structured dictionary learning with group sparsity for human action recognition[J]. Signal Process 112:83–97

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Gastal ESL, Oliveira MM (2012) Adaptive manifolds for real-time high-dimensional filtering. ACM Trans Graph 31(4):1–13

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Heeger DJ, Bergen JR (1995) Pyramid-based texture analysis/synthesis. In: Proceedings of the 22nd annual conference on computer graphics and interactive techniques. ACM, pp 229–238

  18. Li Y, Sharan L, Adelson EH (2005) Compressing and companding high dynamic range images with subband architectures. ACM Trans Graph (TOG) 24(3):836–844

    Article  Google Scholar 

  19. Liu AA, Nie WZ, Gao Y et al (2016) Multi-modal clique-graph matching for view-based 3D model retrieval[J]. IEEE Trans Image Process 25(5):2103–2116

    Article MathSciNet  Google Scholar 

  20. Liu AA, Su YT, Nie WZ et al (2017) Hierarchical clustering multi-task learning for joint human action grouping and recognition[J]. IEEE Trans Pattern Anal Mach Intell 39(1):102–114

    Article  Google Scholar 

  21. Liu Y, Duan J, Wang S et al (2015) Interface MB-based video content editing transcoding[J]. IEEE Trans Circuits Syst Video Technol 25(2):261–274

    Article  Google Scholar 

  22. Nie W, Liu A, Li W et al (2016) Cross-view action recognition by cross-domain learning[J]. Image Vis Comput 55:109–118

    Article  Google Scholar 

  23. Nie W-Z, Liu A-A, Gao Z, Su Y-T (2015) Clique-graph matching by preserving global & local structure. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4503–4510

  24. Nie WZ, Liu AA, Su YT (2016) 3D object retrieval based on sparse coding in weak supervision[J]. J Vis Commun Image Represent 37:40–45

    Article  Google Scholar 

  25. Paris S, Hasinoff SW, Kautz J (2011) Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM Trans Graph 30(4):68

    Article  Google Scholar 

  26. 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 

  27. Singh M, Mandal MK, Basu A (2005) Gaussian and Laplacian of Gaussian weighting functions for robust feature based tracking. Pattern Recogn Lett 26(13):1995–2005

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Sunkavalli K, Johnson MK, Matusik W, Pfister H (2010) Multi-scale image harmonization. ACM Trans Graph (TOG) 29(4):125

    Article  Google Scholar 

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

  31. Tschumperlé D (2006) Fast anisotropic smoothing of multi-valued images using curvature-preserving PDE’s. Int J Comput Vis 68(1):65–82

    Article  Google Scholar 

  32. Wang Q, Tao Y, Lin H (2015) Edge-aware Volume Smoothing Using L0 Gradient Minimization. Comput Graphics Forum 34(3):131–140

    Article  Google Scholar 

  33. Xiaofeng D, Zhenhong S et al (2017) Multiple temple moving object tracking based on mean shift[J]. Comput Eng Appl 53(6):141–144

    Google Scholar 

  34. Xin Y, Zhen-hong S et al (2015) Astronomical image registration combing information entropy and SIFT algorithm[J]. Comput Sci 42(6):57–60

    Google Scholar 

  35. Xu L, Lu C, Xu Y, Jia J (2011) Image smoothing via l0 gradient minimization. ACM Trans Graph 30(6):174

    Google Scholar 

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

    Google Scholar 

  37. Yanxi Z, Zhenhong S et al (2017) Spatiotemporal salient moving object detect in dynamic background[J]. Comput Eng Appl 53(5):170–175

    Google Scholar 

  38. Zhang H, Zha ZJ, Yang Y et al (2013) Attribute-augmented semantic hierarchy: towards bridging semantic gap and intention gap in image retrieval[C]. In: Proceedings of the 21st ACM international conference on Multimedia, vol 2013. ACM, pp 33–42

  39. Zhang Q, Shen X, Xu L, Jia J (2014) Rolling guidance filter[C]. In: European Conference on Computer Vision. Springer, Cham, pp 815–830

  40. Zhang H, Shang X, Luan H et al (2016) Learning from collective intelligence: feature learning using social images and tags[J]. ACM Trans Multimed Comput Commun Appl (TOMM) 13.1:1

    Google Scholar 

  41. Zhang H, Shang X, Yang W et al (2016) Online collaborative learning for open-vocabulary visual classifiers[C]. Proc IEEE Conf Comput Vis Pattern Recognit:2809–2817

  42. Zhao Y, Shang Z, Liu H (2015) Highlight moving object detection based on spatiotemporal saliency in dynamic background. In: Third world conference on complex systems (WCCS). IEEE, pp 1–5

Download references

Acknowledgements

This work is supported by the projects of National Natural Science Foundation of China (11603016, 61540062), the Key Project of Yunnan Applied Basic Research(2014fa021) and project of Research Center of Kunming Forestry Information Engineering Technology(2015FBI06).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, #2, Cuihubei Road, KunMing, 650091, People’s Republic of China

    Zhenping Qiang, Libo He & Dan Xu

  2. Department of Computer and Information Science, Southwest Forestry University, #300, Bailong Road, Kunming, 650224, People’s Republic of China

    Zhenping Qiang, Yaqiong Chen & Xu Chen

Authors
  1. Zhenping Qiang

    You can also search for this author inPubMed Google Scholar

  2. Libo He

    You can also search for this author inPubMed Google Scholar

  3. Yaqiong Chen

    You can also search for this author inPubMed Google Scholar

  4. Xu Chen

    You can also search for this author inPubMed Google Scholar

  5. Dan Xu

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toDan Xu.

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiang, Z., He, L., Chen, Y.et al. Adaptive fast local Laplacian filters and its edge-aware application.Multimed Tools Appl78, 619–639 (2019). https://doi.org/10.1007/s11042-017-5347-9

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp