The Experts below are selected from a list of 58029 Experts worldwide ranked by ideXlab platform
Abderrahim Elmoataz - One of the best experts on this subject based on the ideXlab platform.
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Parameterless discrete regularization on graphs for color Image Filtering
2007Co-Authors: Olivier Lezoray, Sébastien Bougleux, Abderrahim ElmoatazAbstract:A discrete regularization framework on graphs is proposed and studied for color Image Filtering purposes when Images are represented by grid graphs. Image Filtering is considered as a variational problem which consists in minimizing an appropriate energy function. In this paper, we propose a general discrete regularization framework defined on weighted graphs which can be seen as a discrete analogue of classical regularization theory. With this formulation, we propose a family of fast and simple anisotropic linear and nonlinear filters. The parameters of the proposed discrete regularization are estimated to have a parameterless Filtering.
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ICIAR - Parameterless discrete regularization on graphs for color Image Filtering
Lecture Notes in Computer Science, 2007Co-Authors: Olivier Lezoray, Sébastien Bougleux, Abderrahim ElmoatazAbstract:A discrete regularization framework on graphs is proposed and studied for color Image Filtering purposes when Images are represented by grid graphs. Image Filtering is considered as a variational problem which consists in minimizing an appropriate energy function. In this paper, we propose a general discrete regularization framework defined on weighted graphs which can be seen as a discrete analogue of classical regularization theory. With this formulation, we propose a family of fast and simple anisotropic linear and nonlinear filters. The parameters of the proposed discrete regularization are estimated to have a parameterless Filtering.
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.
Yao Jin-liang - One of the best experts on this subject based on the ideXlab platform.
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Improved Sensitive Image Filtering Method
Computer Engineering, 2011Co-Authors: Yao Jin-liangAbstract:Aiming at the shortage that existing sensitive Image Filtering method has higher error rate,this paper presents a sensitive Image Filtering method by combining skin color detection with human detection by Histogram of Gradient(HOG).Extracting features of human bodies by HOG feature,using a detection model trained by Support Vector Machine(SVM) to find out the human body,and then using the skin color detection algorithm to tell whether the Image is sensitive.Experimental results show that this method can detect the sensitive Images under complex background effectively.The accurate rate can achieve 90.2%,the recall rate can achieve 86.3%,and the error rate can achieve 3.5%.
Jean Ponce - One of the best experts on this subject based on the ideXlab platform.
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Robust Guided Image Filtering Using Nonconvex Potentials
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018Co-Authors: Bumsub Ham, Minsu Cho, Jean PonceAbstract:Filtering Images using a guidance signal, a process called guided or joint Image Filtering, has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. This uses an additional guidance signal as a structure prior, and transfers the structure of the guidance signal to an input Image, restoring noisy or altered Image structure. The main drawbacks of such a data-dependent framework are that it does not consider structural differences between guidance and input Images, and that it is not robust to outliers. We propose a novel SD (for static/dynamic) filter to address these problems in a unified framework, and jointly leverage structural information from guidance and input Images. Guided Image Filtering is formulated as a nonconvex optimization problem, which is solved by the majorize-minimization algorithm. The proposed algorithm converges quickly while guaranteeing a local minimum. The SD filter effectively controls the underlying Image structure at different scales, and can handle a variety of types of data from different sensors. It is robust to outliers and other artifacts such as gradient reversal and global intensity shift, and has good edge-preserving smoothing properties. We demonstrate the flexibility and effectiveness of the proposed SD filter in a variety of applications, including depth upsampling, scale-space Filtering, texture removal, flash/non-flash denoising, and RGB/NIR denoising.
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Robust Image Filtering Using Joint Static and Dynamic Guidance
2015Co-Authors: Bumsub Ham, Minsu Cho, Jean PonceAbstract:Regularizing Images under a guidance signal has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint up-sampling. The aim is to transfer fine structures of guidance signals to input Images, restoring noisy or altered structures. One of main drawbacks in such a data-dependent framework is that it does not handle differences in structure between guidance and input Images. We address this problem by jointly leveraging structural information of guidance and input Images. Image Filtering is formulated as a nonconvex optimization problem, which is solved by the majorization-minimization algorithm. The proposed algorithm converges quickly while guaranteeing a local minimum. It effectively controls Image structures at different scales and can handle a variety of types of data from different sensors. We demonstrate the flexibility and effectiveness of our model in several applications including depth super-resolution, scale-space Filtering, texture removal, flash/non- flash denoising, and RGB/NIR denoising.
P.v. Vara Prasad Rao - One of the best experts on this subject based on the ideXlab platform.
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Weighted Guided Image Filtering
International Journal of Research, 2016Co-Authors: Nagara Kavitha, P.v. Vara Prasad RaoAbstract:It is known that local Filtering-based edge preserving smoothing techniques suffer from halo artifacts. In this paper, a weighted guided Image filter (WGIF) is introduced by incorporating an edge-aware weighting into an existing guided Image filter (GIF) to address the problem. The WGIF inherits advantages of both global and local smoothing filters in the sense that: 1) the complexity of the WGIF is O(N) for an Image with N pixels, which is same as the GIF and 2) the WGIF can avoid halo artifacts like the existing global smoothing filters. The WGIF is applied for single Image detail enhancement, single Image haze removal, and fusion of differently exposed Images. Experimental results show that the resultant algorithms produce Images with better visual quality and at the same time halo artifacts can be reduced/avoided from appearing in the final Images with negligible increment on running times.
