Multimedia Tools and Applications

, Volume 78, Issue 1, pp 619–639 | Cite as

Adaptive fast local Laplacian filters and its edge-aware application

  • Zhenping Qiang
  • Libo He
  • Yaqiong Chen
  • Xu Chen
  • Dan XuEmail author


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.


Laplacian pyramid Detail enhancement Detail smoothing Image editing Local Laplacian filters 



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).


  1. 1.
    Arnheim R (1956) Art and visual perception: A psychology of the creative eye, vol 16.3. University of California Press, Oakland, p 425Google Scholar
  2. 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 YorkzbMATHGoogle Scholar
  3. 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):167CrossRefGoogle Scholar
  4. 4.
    Aurich V, Weule J (1995) Non-linear gaussian filters performing edge preserving diffusion. In: Mustererkennung 1995. Springer, Berlin, pp 538–545Google Scholar
  5. 5.
    Barash D (2002) Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation. IEEE Trans Pattern Anal Mach Intell 24(6):844–847CrossRefGoogle Scholar
  6. 6.
    Burt P, Adelson E (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540CrossRefGoogle Scholar
  7. 7.
    Chen J, Paris S, Durand F (2007) Real-time edge-aware image processing with the bilateral grid. ACM Trans Graph (TOG) 26(3):103CrossRefGoogle Scholar
  8. 8.
    Du H, Jin X, Willis PJ (2016) Two-level joint local laplacian texture filtering. Vis Comput 32(12):1537–1548CrossRefGoogle Scholar
  9. 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):67CrossRefGoogle Scholar
  10. 10.
    Fattal R (2009) Edge-avoiding wavelets and their applications. ACM Trans Graph (TOG) 28(3):22CrossRefGoogle Scholar
  11. 11.
    Fattal R, Agrawala M, Rusinkiewicz S (2007) Multiscale shape and detail enhancement from multi-light image collections. ACM Trans Graph 26(3):51CrossRefGoogle Scholar
  12. 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 151:554–564CrossRefGoogle Scholar
  13. 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 112:83–97CrossRefGoogle Scholar
  14. 14.
    Gastal ESL, Oliveira MM (2011) Domain transform for edge-aware image and video processing. ACM Trans Graph (ToG) 30(4):69CrossRefGoogle Scholar
  15. 15.
    Gastal ESL, Oliveira MM (2012) Adaptive manifolds for real-time high-dimensional filtering. ACM Trans Graph 31(4):1–13CrossRefGoogle Scholar
  16. 16.
    He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409CrossRefGoogle Scholar
  17. 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 229–238Google Scholar
  18. 18.
    Li Y, Sharan L, Adelson EH (2005) Compressing and companding high dynamic range images with subband architectures. ACM Trans Graph (TOG) 24(3):836–844CrossRefGoogle Scholar
  19. 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):2103–2116MathSciNetCrossRefGoogle Scholar
  20. 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):102–114CrossRefGoogle Scholar
  21. 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):261–274CrossRefGoogle Scholar
  22. 22.
    Nie W, Liu A, Li W et al (2016) Cross-view action recognition by cross-domain learning[J]. Image Vis Comput 55:109–118CrossRefGoogle Scholar
  23. 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 4503–4510Google Scholar
  24. 24.
    Nie WZ, Liu AA, Su YT (2016) 3D object retrieval based on sparse coding in weak supervision[J]. J Vis Commun Image Represent 37:40–45CrossRefGoogle Scholar
  25. 25.
    Paris S, Hasinoff SW, Kautz J (2011) Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM Trans Graph 30(4):68CrossRefGoogle Scholar
  26. 26.
    Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639CrossRefGoogle Scholar
  27. 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):1995–2005CrossRefGoogle Scholar
  28. 28.
    Subr K, Soler C, Durand F (2009) Edge-preserving multiscale image decomposition based on local extrema. ACM Trans Graph (TOG) 28(5):147CrossRefGoogle Scholar
  29. 29.
    Sunkavalli K, Johnson MK, Matusik W, Pfister H (2010) Multi-scale image harmonization. ACM Trans Graph (TOG) 29(4):125CrossRefGoogle Scholar
  30. 30.
    Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Sixth international conference on computer vision. IEEE, pp 839–846Google Scholar
  31. 31.
    Tschumperlé D (2006) Fast anisotropic smoothing of multi-valued images using curvature-preserving PDE’s. Int J Comput Vis 68(1):65–82CrossRefGoogle Scholar
  32. 32.
    Wang Q, Tao Y, Lin H (2015) Edge-aware Volume Smoothing Using L0 Gradient Minimization. Comput Graphics Forum 34(3):131–140CrossRefGoogle Scholar
  33. 33.
    Xiaofeng D, Zhenhong S et al (2017) Multiple temple moving object tracking based on mean shift[J]. Comput Eng Appl 53(6):141–144Google Scholar
  34. 34.
    Xin Y, Zhen-hong S et al (2015) Astronomical image registration combing information entropy and SIFT algorithm[J]. Comput Sci 42(6):57–60Google Scholar
  35. 35.
    Xu L, Lu C, Xu Y, Jia J (2011) Image smoothing via l0 gradient minimization. ACM Trans Graph 30(6):174Google Scholar
  36. 36.
    Xu L, Yan Q, Xia Y, Jia J (2012) Structure extraction from texture via relative total variation. ACM Trans Graph (TOG) 31(6):139Google Scholar
  37. 37.
    Yanxi Z, Zhenhong S et al (2017) Spatiotemporal salient moving object detect in dynamic background[J]. Comput Eng Appl 53(5):170–175Google Scholar
  38. 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 33–42Google Scholar
  39. 39.
    Zhang Q, Shen X, Xu L, Jia J (2014) Rolling guidance filter[C]. In: European Conference on Computer Vision. Springer, Cham, pp 815–830Google Scholar
  40. 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) 13.1:1Google Scholar
  41. 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:2809–2817Google Scholar
  42. 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 1–5Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Zhenping Qiang
    • 1
    • 2
  • Libo He
    • 1
  • Yaqiong Chen
    • 2
  • Xu Chen
    • 2
  • Dan Xu
    • 1
    Email author
  1. 1.School of Information Science and EngineeringYunnan UniversityKunMingPeople’s Republic of China
  2. 2.Department of Computer and Information ScienceSouthwest Forestry UniversityKunmingPeople’s Republic of China

Personalised recommendations