The Experts below are selected from a list of 5499 Experts worldwide ranked by ideXlab platform
Xiaoou Tang - One of the best experts on this subject based on the ideXlab platform.
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guided image filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Jian Sun, Xiaoou TangAbstract: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 [1], 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.
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guided image filtering
European Conference on Computer Vision, 2010Co-Authors: Jian Sun, Xiaoou TangAbstract:In this paper, we propose a novel type of explicit image filter - guided filter. Derived from a local linear model, the guided filter generates 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 perform as an edge-preserving Smoothing Operator like the popular bilateral filter [1], but has better behavior near the edges. It also has a theoretical connection with the matting Laplacian matrix [2], so is a more generic concept than a Smoothing Operator and can better utilize the structures in the guidance image. Moreover, the guided filter has a fast and non-approximate linear-time algorithm, whose computational complexity is independent of the filtering kernel size. We demonstrate that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail Smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling.
Jian Sun - One of the best experts on this subject based on the ideXlab platform.
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guided image filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Jian Sun, Xiaoou TangAbstract: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 [1], 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.
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guided image filtering
European Conference on Computer Vision, 2010Co-Authors: Jian Sun, Xiaoou TangAbstract:In this paper, we propose a novel type of explicit image filter - guided filter. Derived from a local linear model, the guided filter generates 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 perform as an edge-preserving Smoothing Operator like the popular bilateral filter [1], but has better behavior near the edges. It also has a theoretical connection with the matting Laplacian matrix [2], so is a more generic concept than a Smoothing Operator and can better utilize the structures in the guidance image. Moreover, the guided filter has a fast and non-approximate linear-time algorithm, whose computational complexity is independent of the filtering kernel size. We demonstrate that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail Smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling.
Frédéric Dufaux - One of the best experts on this subject based on the ideXlab platform.
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An image Smoothing Operator for fast and accurate scale space approximation
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Maxim Karpushin, Giuseppe Valenzise, Frédéric DufauxAbstract:Gussian image Smoothing is a fundamental operation in the extraction of scale-invariant feature points. Its computation, however, can be too expensive in some resource-constrained scenarios. Alternative solutions such as the box filter can be computed more efficiently, at the cost of a loss in feature repeatibility under some conditions. In this paper we propose a fast and accurate image Smoothing Operator based on integral images. It has the same order of computational complexity as the box filter, but provides much more accurate visual results and improved keypoint repeatability, which is confirmed in a feature detection scenario using SIFT features.
Richard Szeliski - One of the best experts on this subject based on the ideXlab platform.
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Edge-preserving decompositions for multi-scale tone and detail manipulation
ACM Transactions on Graphics, 2008Co-Authors: Zeev Farbman, Raanan Fattal, Dani Lischinski, Richard SzeliskiAbstract:Many recent computational photography techniques decompose an image into a piecewise smooth base layer, containing large scale variations in intensity, and a residual detail layer capturing the smaller scale details in the image. In many of these applications, it is important to control the spatial scale of the extracted details, and it is often desirable to manipulate details at multiple scales, while avoiding visual artifacts. In this paper we introduce a new way to construct edge-preserving multi-scale image decompositions. We show that current basedetail decomposition techniques, based on the bilateral filter, are limited in their ability to extract detail at arbitrary scales. Instead, we advocate the use of an alternative edge-preserving Smoothing Operator, based on the weighted least squares optimization framework, which is particularly well suited for progressive coarsening of images and for multi-scale detail extraction. After describing this Operator, we show how to use it to construct edge-preserving multi-scale decompositions, and compare it to the bilateral filter, as well as to other schemes. Finally, we demonstrate the effectiveness of our edge-preserving decompositions in the context of LDR and HDR tone mapping, detail enhancement, and other applications.
Hiba Nassar - One of the best experts on this subject based on the ideXlab platform.
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a consistent estimator of the Smoothing Operator in the functional hodrick prescott filter
Communications in Statistics-theory and Methods, 2018Co-Authors: Hiba NassarAbstract:In this paper, we consider a version of the functional Hodrick-Prescott filter for functional time series. We show that the associated optimal Smoothing Operator preserves the ‘noise-to-signal ratio’ structure. Moreover, as the main result, we propose a consistent estimator of this optimal Smoothing Operator.
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a consistent estimator of the Smoothing Operator in the functional hodrick prescott filter
arXiv: Statistics Theory, 2015Co-Authors: Hiba NassarAbstract:In this paper we consider a version of the functional Hodrick-Prescott ?filter for functional time series. We show that the associated optimal Smoothing Operator preserves the 'noise-to-signal' structure. Moreover, we propose a consistent estimator of this optimal Smoothing Operator.
