Issue No. 06 - June (2013 vol. 35)
Kaiming He , Microsoft Research Asia, Beijing
Jian Sun , Microsoft Research Asia, Beijing
Xiaoou Tang , The Chinese University of Hong Kong, Shatin
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 it 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 nonapproximate 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.
Image edge detection, Kernel, Smoothing methods, Joints, Histograms, Laplace equations, Jacobian matrices, linear time filtering, Edge-preserving filtering, bilateral filter
K. He, J. Sun and X. Tang, "Guided Image Filtering," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. , pp. 1397-1409, 2013.