1017Accesses
9Citations
Abstract
As one of the most fundamental operations in computer graphics and computer vision, sharpness enhancement can enhance an image in respect of sharpness characteristics. Unfortunately, the prevalent methods often fail to eliminate image noise, unrealistic details, or incoherent enhancement. In this paper, we propose a new sharpness enhancement approach that can boost the sharpness characteristics of an image effectively with affinity-based edge preserving. Our approach includes three gradient-domain operations: sharpness saliency representation, affinity-based gradient transformation, and gradient-domain image reconstruction. Moreover, we also propose an evaluation method based on sharpness distribution for analyzing all sharpness enhancement approaches in respect of sharpness characteristics. By evaluating the sharpness distribution and comparing the visual appearance, we demonstrate the effectiveness of our sharpness enhancement approach.
This is a preview of subscription content,log in via an institution to check access.
Access this article
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
Price includes VAT (Japan)
Instant access to the full article PDF.










Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley, Boston (2001)
Inc. Adobe, Systems. Photoshop CS 5, 2010
Elad, M.: On the origin of the bilateral filter and ways to improve it. IEEE Trans. Image Process.11, 1141–1151 (2002)
Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph.27, 67 (2008)
Fattal, R.: Edge-avoiding wavelets and their applications. ACM Trans. Graph.28(3), 1–10 (2009)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision. ICCV ’98, Washington, DC, USA, pp. 839–846. IEEE Computer Society, Los Alamitos (1998)
He, K., Sun, J., Tang, X.: Guided image filtering. In: Proceedings of the 11th European Conference on Computer vision: Part I, ECCV’10, pp. 1–14. Springer, Berlin (2010)
Paris, S., Hasinoff, S.W., Kautz, J.: Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. In: ACM SIGGRAPH, SIGGRAPH ’11, New York, NY, USA, 2011. ACM, New York (2011)
Zeng, X., Chen, W., Peng, Q.: A novel variational image model: towards a unified approach to image editing. J. Comput. Sci. Technol. 224–231 (2006)
Bhat, P., Curless, B., Cohen, M., Zitnick, C.L.: Fourier analysis of the 2d screened Poisson equation for gradient domain problems. In: Proceedings of the 10th European Conference on Computer Vision: Part II, pp. 114–128. Springer, Berlin (2008)
Bhat, P., Zitnick, C.L., Cohen, M., Curless, B.: Gradientshop: a gradient-domain optimization framework for image and video filtering. ACM Trans. Graph.29, 10:1–10:14 (2010)
Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. ACM Trans. Graph.21, 249–256 (2002)
Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Trans. Graph.22, 313–318 (2003)
Levin, A., Zomet, A., Peleg, S., Weiss, Y.: Seamless image stitching in the gradient domain. In: Eighth European Conference on Computer Vision (ECCV 2004), pp. 377–389. Springer, Berlin (2003)
Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM Trans. Graph.23, 689–694 (2004)
Lischinski, D., Farbman, Z., Uyttendaele, M., Szeliski, R.: Interactive local adjustment of tonal values. ACM Trans. Graph.25, 646–653 (2006)
Orzan, A., Bousseau, A., Barla, P., Thollot, J.: Structure-preserving manipulation of photographs. In: Proceedings of the 5th International Symposium on Non-photorealistic Animation and Rendering, NPAR ’07, New York, NY, USA, 2007, pp. 103–110. ACM, New York (2007)
Agrawal, A., Raskar, R., Nayar, S.K., Li, Y.: Removing photography artifacts using gradient projection and flash-exposure sampling. ACM Trans. Graph.24, 828–835 (2005)
Agrawal, A., Raskar, R., Chellappa, R.: Edge suppression by gradient field transformation using cross-projection tensors. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition—vol. 2, CVPR ’06, Washington, DC, USA, 2006, pp. 2301–2308. IEEE Computer Society, Los Alamitos (2006)
Agrawal, A., Raskar, R.: What is the range of surface reconstructions from a gradient field. In: ECCV, pp. 578–591. Springer, Berlin (2006)
Ding, M., Tong, R.f.: Content-aware copying and pasting in images. Vis. Comput.26(6–8), 721–729 (2010)
Xie, Z.-F., Shen, Y., Ma, L., Chen, Z.: Seamless video composition using optimized mean-value cloning. Vis. Comput.26(6–8), 1123–1134 (2010)
Zhang, Y., Tong, R.: Environment-sensitive cloning in images. Vis. Comput.27, 739–748 (2011)
Fattal, R.: Image upsampling via imposed edge statistics. ACM Trans. Graph.26(3), 95 (2007)
Sun, J., Xu, Z., Shum, H.-Y.: Image super-resolution using gradient profile prior. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell.30, 228–242 (2008)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1956–1963 (2009)
Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: 8th IEEE International Conference on Computer Vision, vol. 1, pp. 105–112 (2001)
Rother, C., Kolmogorov, V., Blake, A.: Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph.23, 309–314 (2004)
Wang, J., Cohen, M.F.: Optimized color sampling for robust matting. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR ’07, pp. 1–8 (2007)
He, K., Sun, J., Tang, X.: Fast matting using large kernel matting Laplacian matrices. In: IEEE Conference on Computer Vision and Pattern Recognition (2010)
Acknowledgements
We would like to thank the anonymous reviewers for their valuable comments. This work was supported by the National Basic Research Project of China (No. 2011CB302203), the National Natural Science Foundation of China (No. 61073089, No. 61133009), the Innovation Program of the Science and Technology Commission of Shanghai Municipality (No. 10511501200), and a SRG grant from City University of Hong Kong (No. 7002664).
Author information
Authors and Affiliations
Shanghai Jiaotong University, Shanghai, China
Zhi-Feng Xie, Yan Gui, Min-Gang Chen & Li-Zhuang Ma
City University of Hong Kong, Kowloon, Hong Kong
Rynson W. H. Lau
- Zhi-Feng Xie
You can also search for this author inPubMed Google Scholar
- Rynson W. H. Lau
You can also search for this author inPubMed Google Scholar
- Yan Gui
You can also search for this author inPubMed Google Scholar
- Min-Gang Chen
You can also search for this author inPubMed Google Scholar
- Li-Zhuang Ma
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toZhi-Feng Xie.
Rights and permissions
About this article
Cite this article
Xie, ZF., Lau, R.W.H., Gui, Y.et al. A gradient-domain-based edge-preserving sharpen filter.Vis Comput28, 1195–1207 (2012). https://doi.org/10.1007/s00371-011-0668-6
Published:
Issue Date:
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative