“Local Laplacian filters: edge-aware image processing with a Laplacian pyramid” by Paris, Hasinoff and Kautz

The Laplacian pyramid is ubiquitous for decomposing images into multiple scales and is widely used for image analysis. However, because it is constructed with spatially invariant Gaussian kernels, the Laplacian pyramid is widely believed as being unable to represent edges well and as being ill-suited for edge-aware operations such as edge-preserving smoothing and tone mapping. To tackle these tasks, a wealth of alternative techniques and representations have been proposed, e.g., anisotropic diffusion, neighborhood filtering, and specialized wavelet bases. While these methods have demonstrated successful results, they come at the price of additional complexity, often accompanied by higher computational cost or the need to post-process the generated results. In this paper, we show state-of-the-art edge-aware processing using standard Laplacian pyramids. We characterize edges with a simple threshold on pixel values that allows us to differentiate large-scale edges from small-scale details. Building upon this result, we propose a set of image filters to achieve edge-preserving smoothing, detail enhancement, tone mapping, and inverse tone mapping. The advantage of our approach is its simplicity and flexibility, relying only on simple point-wise nonlinearities and small Gaussian convolutions; no optimization or post-processing is required. As we demonstrate, our method produces consistently high-quality results, without degrading edges or introducing halos.

1. Aubert, G., and Kornprobst, P. 2002. Mathematical problems in image processing: Partial Differential Equations and the Calculus of Variations, vol. 147 of Applied Mathematical Sciences. Springer. Google Scholar
2. Bae, S., Paris, S., and Durand, F. 2006. Two-scale tone management for photographic look. ACM Transactions on Graphics (Proc. SIGGRAPH) 25, 3, 637–645. Google ScholarDigital Library
3. Bhat, P., Zitnick, C. L., Cohen, M., and Curless, B. 2010. Gradientshop: A gradient-domain optimization framework for image and video filtering. ACM Transactions on Graphics 29, 2. Google ScholarDigital Library
4. Buades, A., Coll, B., and Morel, J.-M. 2006. The staircasing effect in neighborhood filters and its solution. IEEE Transactions on Image Processing 15, 6, 1499–1505. Google ScholarDigital Library
5. Burt, P. J., and Adelson, E. H. 1983. The Laplacian pyramid as a compact image code. IEEE Transactions on Communication 31, 4, 532–540.Google ScholarCross Ref
6. Chen, J., Paris, S., and Durand, F. 2007. Real-time edge-aware image processing with the bilateral grid. ACM Transactions on Graphics (Proc. SIGGRAPH) 26, 3. Google ScholarDigital Library
7. Criminisi, A., Sharp, T., Rother, C., and Perez, P. 2010. Geodesic image and video editing. ACM Transactions on Graphics 29, 5. Google ScholarDigital Library
8. Dippel, S., Stahl, M., Wiemker, R., and Blaffert, T. 2002. Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform. IEEE Transactions on Medical Imaging 21, 4.Google ScholarCross Ref
9. Durand, F., and Dorsey, J. 2002. Fast bilateral filtering for the display of high-dynamic-range images. ACM Transactions on Graphics (Proc. SIGGRAPH) 21, 3. Google ScholarDigital Library
10. Farbman, Z., Fattal, R., Lischinski, D., and Szeliski, R. 2008. Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics (Proc. SIGGRAPH) 27, 3. Google ScholarDigital Library
11. Fattal, R., Lischinski, D., and Werman, M. 2002. Gradient domain high dynamic range compression. ACM Transactions on Graphics (Proc. SIGGRAPH) 21, 3. Google ScholarDigital Library
12. Fattal, R., Agrawala, M., and Rusinkiewicz, S. 2007. Multiscale shape and detail enhancement from multi-light image collections. ACM Transactions on Graphics (Proc. SIGGRAPH) 26, 3. Google ScholarDigital Library
13. Fattal, R., Carroll, R., and Agrawala, M. 2009. Edge-based image coarsening. ACM Transactions on Graphics 29, 1. Google ScholarDigital Library
14. Fattal, R. 2009. Edge-avoiding wavelets and their applications. ACM Transactions on Graphics (Proc. SIGGRAPH) 28, 3. Google ScholarDigital Library
15. He, K., Sun, J., and Tang, X. 2010. Guided image filtering. In Proceedings of European Conference on Computer Vision. Google ScholarDigital Library
16. Heeger, D. J., and Bergen, J. R. 1995. Pyramid-based texture analysis/synthesis. In Proceedings of the ACM SIGGRAPH conference. Google Scholar
17. Kass, M., and Solomon, J. 2010. Smoothed local histogram filters. ACM Transactions on Graphics (Proc. SIGGRAPH) 29, 3. Google ScholarDigital Library
18. Kimmel, R. 2003. Numerical Geometry of Images: Theory, Algorithms, and Applications. Springer. ISBN 0387955623. Google Scholar
19. Li, Y., Sharan, L., and Adelson, E. H. 2005. Compressing and companding high dynamic range images with subband architectures. ACM Transactions on Graphics (Proc. SIGGRAPH) 24, 3. Google Scholar
20. Lischinski, D., Farbman, Z., Uyttendaele, M., and Szeliski, R. 2006. Interactive local adjustment of tonal values. ACM Transactions on Graphics (Proc. SIGGRAPH) 25, 3. Google ScholarDigital Library
21. Mantiuk, R., Myszkowski, K., and Seidel, H.-P. 2006. A perceptual framework for contrast processing of high dynamic range images. ACM Transactions on Applied Perception 3, 3, 286–308. Google ScholarDigital Library
22. Mantiuk, R., Mantiuk, R., Tomaszewska, A., and Heidrich, W. 2009. Color correction for tone mapping. Computer Graphics Forum (Proc. Eurographics) 28, 2, 193–202.Google ScholarCross Ref
23. Masia, B., Agustin, S., Fleming, R. W., Sorkine, O., and Gutierrez, D. 2009. Evaluation of reverse tone mapping through varying exposure conditions. ACM Transactions on Graphics (Proc. SIGGRAPH Asia) 28, 5. Google Scholar
24. Paris, S., and Durand, F. Tone-mapping code. http://people.csail.mit.edu/sparis/code/src/tone_mapping.zip. Accessed on January 14th, 2011.Google Scholar
25. Paris, S., Kornprobst, P., Tumblin, J., and Durand, F. 2009. Bilateral filtering: Theory and applications. Foundations and Trends in Computer Graphics and Vision. Google Scholar
26. Perona, P., and Malik, J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions Pattern Analysis Machine Intelligence 12, 7, 629–639. Google ScholarDigital Library
27. Reinhard, E., Stark, M., Shirley, P., and Ferwerda, J. 2002. Photographic tone reproduction for digital images. ACM Transactions on Graphics (Proc. SIGGRAPH) 21, 3. Google ScholarDigital Library
28. Subr, K., Soler, C., and Durand, F. 2009. Edge-preserving multiscale image decomposition based on local extrema. ACM Transactions on Graphics (Proc. SIGGRAPH Asia) 28, 5. Google ScholarDigital Library
29. Sunkavalli, K., Johnson, M. K., Matusik, W., and Pfister, H. 2010. Multi-scale image harmonization. ACM Transactions on Graphics (Proc. SIGGRAPH) 29, 3. Google ScholarDigital Library
30. Szeliski, R. 2006. Locally adapted hierarchical basis preconditioning. ACM Transactions on Graphics (Proc. SIGGRAPH) 25, 3. Google ScholarDigital Library
31. Tomasi, C., and Manduchi, R. 1998. Bilateral filtering for gray and color images. In Proceedings of the International Conference on Computer Vision, IEEE, 839–846. Google ScholarDigital Library
32. Tumblin, J., and Turk, G. 1999. Low curvature image simplifiers (LCIS): A boundary hierarchy for detail-preserving contrast reduction. In Proceedings of SIGGRAPH. Google Scholar
33. Vuylsteke, P., and Schoeters, E. P. 1994. Multiscale image contrast amplification (MUSICA). In Proceedings SPIE, vol. 2167, 551–560.Google Scholar
34. Witkin, A., Terzopoulos, D., and Kass, M. 1987. Signal matching through scale space. International Journal of Computer Vision 1, 2, 759–764.Google ScholarCross Ref
35. Witkin, A. 1983. Scale-space filtering. In Proceedings of the International Joint Conference on Artificial Intelligence, vol. 2, 1019–1022. Google Scholar