Edge-avoiding wavelets and their applications

By Raanan Fattal. ACM Transactions on Graphics, 2009

313 citations recorded by the source corpus.

How researchers see this paper

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Transfusive image manipulation (2012, methodology): “[Fattal 2009; Fattal et al. 2009]), but the locality and sparsity properties of our weights are more suitable for detailed warps.”
Fast Image Processing with Fully-Convolutional Networks (2017, background): “Techniques developed in the last decade can dramatically enhance detail [24, 69, 26, 28, 60], transform the image by applying a master photographer’s style [7, 5], smooth the image for the purpose of abstraction [73, 76, 79], and eliminate the effects of atmospheric scattering [25, 35, 27, 9].”
Guided Image Filtering (2010, background): “The O ð N Þ time Edge-Avoiding Wavelets (EAW) [37] are wavelets with explicit image-adaptive weights.”
Image smoothing via L0 gradient minimization (2011, methodology): “Edge-preserving smoothing can be achieved by local filtering, including bilateral filtering [Tomasi and Manduchi 1998], its accelerated versions [Paris and Durand 2006; Weiss 2006; Chen et al. 2007] and relatives [Choudhury and Tumblin 2003; Fattal 2009; Baek and Jacobs 2010; Kass and Solomon 2010].”
Fast Global Image Smoothing Based on Weighted Least Squares (2014, background): “D. Min and J. Lu are with the Advanced Digital Sciences Center, Singapore (e-mail: dongbo@adsc.com.sg; jiangbo.lu@adsc.com.sg).”