Guided Image Filtering
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Abstract In this paper we propose a novel explicit image filter called guided filter. Derived from a local linear model, the guided filter computes the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter, but has better behaviors near edges. The guided filter is also a more generic concept beyond smoothing: it can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and non-approximate linear time algorithm, regardless of the kernel size and the intensity range. Currently it is one of the fastest edge-preserving filters. Experiments show that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications including edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, joint upsampling, etc. A personal comment: Try the guided filter in any situation when the bilateral filter works well. The guided filter is much faster and sometimes (though not always) works even better. |
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Publications: Guided Image Filtering, by Kaiming He, Jian Sun, and Xiaoou Tang, in ECCV 2010 (Oral). Guided Image Filtering, by Kaiming He, Jian Sun, and Xiaoou Tang, in TPAMI 2012. Other materials: ECCV 2010 presentation slides Code See also: Fast Cost-Volume Filtering for
Visual Correspondence and Beyond, in CVPR 2011 Linear stereo matching, in ICCV
2011
http://www.vision.deis.unibo.it/smatt/ICCV2011_LinearStereo.html Adaptive Manifolds for Real-Time
High-Dimensional Filtering, in SIGGRAPH 2012 |
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