The Experts below are selected from a list of 51144 Experts worldwide ranked by ideXlab platform
Dacheng Tao - One of the best experts on this subject based on the ideXlab platform.
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An Underwater Image Enhancement Benchmark Dataset and Beyond
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2019Co-Authors: Chunle Guo, Wenqi Ren, Runmin Cong, Junhui Hou, Sam Kwong, Dacheng TaoAbstract:Underwater Image Enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater Image Enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world Images. It is thus unclear how these algorithms would perform on Images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater Image Enhancement using large-scale real-world Images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater Images, 890 of which have the corresponding reference Images. We treat the rest 60 underwater Images which cannot obtain satisfactory reference Images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater Image Enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater Image Enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater Image Enhancement. The dataset and code are available at https://li-chongyi.github.io/proj_benchmark.html .
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An Underwater Image Enhancement Benchmark Dataset and Beyond
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Chunle Guo, Wenqi Ren, Runmin Cong, Junhui Hou, Sam Kwong, Dacheng TaoAbstract:Underwater Image Enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater Image Enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world Images. It is thus unclear how these algorithms would perform on Images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater Image Enhancement using large-scale real-world Images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater Images, 890 of which have the corresponding reference Images. We treat the rest 60 underwater Images which cannot obtain satisfactory reference Images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater Image Enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater Image Enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater Image Enhancement. The dataset and code are available at this https URL.
Dani Lischinski - One of the best experts on this subject based on the ideXlab platform.
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Personalization of Image Enhancement
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Sing Bing Kang, Dani LischinskiAbstract:We address the problem of incorporating user preference in automatic Image Enhancement. Unlike generic tools for automatically enhancing Images, we seek to develop methods that can first observe user preferences on a training set, and then learn a model of these preferences to personalize Enhancement of unseen Images. The challenge of designing such system lies at intersection of computer vision, learning, and usability; we use techniques such as active sensor selection and distance metric learning in order to solve the problem. The experimental evaluation based on user studies indicates that different users do have different preferences in Image Enhancement, which suggests that personalization can further help improve the subjective quality of generic Image Enhancements.
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CVPR - Personalization of Image Enhancement
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Sing Bing Kang, Ashish Kapoor, Dani LischinskiAbstract:We address the problem of incorporating user preference in automatic Image Enhancement. Unlike generic tools for automatically enhancing Images, we seek to develop methods that can first observe user preferences on a training set, and then learn a model of these preferences to personalize Enhancement of unseen Images. The challenge of designing such system lies at intersection of computer vision, learning, and usability; we use techniques such as active sensor selection and distance metric learning in order to solve the problem. The experimental evaluation based on user studies indicates that different users do have different preferences in Image Enhancement, which suggests that personalization can further help improve the subjective quality of generic Image Enhancements.
Sing Bing Kang - One of the best experts on this subject based on the ideXlab platform.
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CVPR - Collaborative personalization of Image Enhancement
CVPR 2011, 2011Co-Authors: Juan C. Caicedo, Ashish Kapoor, Sing Bing KangAbstract:While most existing Enhancement tools for photographs have universal auto-Enhancement functionality, recent research [8] shows that users can have personalized preferences. In this paper, we explore whether such personalized preferences in Image Enhancement tend to cluster and whether users can be grouped according to such preferences. To this end, we analyze a comprehensive data set of Image Enhancements collected from 336 users via Amazon Mechanical Turk. We find that such clusters do exist and can be used to derive methods to learn statistical preference models from a group of users. We also present a probabilistic framework that exploits the ideas behind collaborative filtering to automatically enhance novel Images for new users. Experiments show that inferring clusters in Image Enhancement preferences results in better prediction of Image Enhancement preferences and outperforms generic auto-correction tools.
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Personalization of Image Enhancement
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Sing Bing Kang, Dani LischinskiAbstract:We address the problem of incorporating user preference in automatic Image Enhancement. Unlike generic tools for automatically enhancing Images, we seek to develop methods that can first observe user preferences on a training set, and then learn a model of these preferences to personalize Enhancement of unseen Images. The challenge of designing such system lies at intersection of computer vision, learning, and usability; we use techniques such as active sensor selection and distance metric learning in order to solve the problem. The experimental evaluation based on user studies indicates that different users do have different preferences in Image Enhancement, which suggests that personalization can further help improve the subjective quality of generic Image Enhancements.
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CVPR - Personalization of Image Enhancement
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Sing Bing Kang, Ashish Kapoor, Dani LischinskiAbstract:We address the problem of incorporating user preference in automatic Image Enhancement. Unlike generic tools for automatically enhancing Images, we seek to develop methods that can first observe user preferences on a training set, and then learn a model of these preferences to personalize Enhancement of unseen Images. The challenge of designing such system lies at intersection of computer vision, learning, and usability; we use techniques such as active sensor selection and distance metric learning in order to solve the problem. The experimental evaluation based on user studies indicates that different users do have different preferences in Image Enhancement, which suggests that personalization can further help improve the subjective quality of generic Image Enhancements.
Chunle Guo - One of the best experts on this subject based on the ideXlab platform.
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An Underwater Image Enhancement Benchmark Dataset and Beyond
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2019Co-Authors: Chunle Guo, Wenqi Ren, Runmin Cong, Junhui Hou, Sam Kwong, Dacheng TaoAbstract:Underwater Image Enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater Image Enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world Images. It is thus unclear how these algorithms would perform on Images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater Image Enhancement using large-scale real-world Images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater Images, 890 of which have the corresponding reference Images. We treat the rest 60 underwater Images which cannot obtain satisfactory reference Images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater Image Enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater Image Enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater Image Enhancement. The dataset and code are available at https://li-chongyi.github.io/proj_benchmark.html .
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An Underwater Image Enhancement Benchmark Dataset and Beyond
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Chunle Guo, Wenqi Ren, Runmin Cong, Junhui Hou, Sam Kwong, Dacheng TaoAbstract:Underwater Image Enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater Image Enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world Images. It is thus unclear how these algorithms would perform on Images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater Image Enhancement using large-scale real-world Images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater Images, 890 of which have the corresponding reference Images. We treat the rest 60 underwater Images which cannot obtain satisfactory reference Images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater Image Enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater Image Enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater Image Enhancement. The dataset and code are available at this https URL.
Huang Nan - One of the best experts on this subject based on the ideXlab platform.
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Application Research of Genetic Algorithm Image Enhancement
Computer Simulation, 2012Co-Authors: Huang NanAbstract:Image Enhancement in Image processing is a classic problem.The paper put forward an algorithm for Image Enhancement based on genetic algorithm.The method uses the genetic algorithm to automatically select two lines of inflection point position and piecewise linear slope,and gets optimal piecewise linear transformation curve,so as to realize the Image Enhancement processing.Simulation results show that,compared with other Enhancement methods,this method not only improves the overall Image brightness,but also enhances the Image contrast and detail,with a certain ability to suppress the noise in Image Enhancement processing.
