The Experts below are selected from a list of 57789 Experts worldwide ranked by ideXlab platform
Eiji Uchino - One of the best experts on this subject based on the ideXlab platform.
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IECON - Impulse noise removal by using one-dimensional switching median filter applied along space-filling curve reflecting structural context of Image
IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013Co-Authors: Takanori Koga, Noriaki Suetake, Tsuyoshi Kato, Eiji UchinoAbstract:A switching median filter (SMF) is effective for impulse noise removal while still preserving edges in an Input Image. This filter firstly detects pixels corrupted by impulse noise and then filters only the noise-corrupted pixels. However the noise detection process does not always work perfectly. Particularly, pixels constituting thin lines in an Input Image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered despite the needlessness of the filtering. As the result of the filtering, the Image might be over-smoothed and be deteriorated throughout the entire Image. To cope with this problem, we propose a new impulse noise removal method based on a one-dimensional SMF and a space-filling curve which reflects structural contexts of an Input Image. The effectiveness of the proposed method is verified by some experiments.
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Impulse noise removal by using one-dimensional switching median filter applied along space-filling curve reflecting structural context of Image
IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013Co-Authors: Takanori Koga, Noriaki Suetake, Tsuyoshi Kato, Eiji UchinoAbstract:A switching median filter (SMF) is effective for impulse noise removal while still preserving edges in an Input Image. This filter firstly detects pixels corrupted by impulse noise and then filters only the noise-corrupted pixels. However the noise detection process does not always work perfectly. Particularly, pixels constituting thin lines in an Input Image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered despite the needlessness of the filtering. As the result of the filtering, the Image might be over-smoothed and be deteriorated throughout the entire Image. To cope with this problem, we propose a new impulse noise removal method based on a one-dimensional SMF and a space-filling curve which reflects structural contexts of an Input Image. The effectiveness of the proposed method is verified by some experiments.
Takanori Koga - One of the best experts on this subject based on the ideXlab platform.
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Impulse noise removal using 1-D switching median filter with adaptive scanning order based on structural context of Image
Optical Review, 2015Co-Authors: Takanori Koga, Noriaki SuetakeAbstract:This paper describes the detail-preserving impulse noise removal performance of a one-dimensional (1-D) switching median filter (SMF) applied along an adaptive space-filling curve. Usually, a SMF with a two-dimensional (2-D) filter window is widely used for impulse noise removal while still preserving detailed parts in an Input Image. However, the noise detector of the 2-D filter does not always distinguish between the original pixels and the noise-corrupted ones perfectly. In particular, pixels constituting thin lines in an Input Image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered regardless of the necessity of the filtering. To cope with this problem, we propose a new impulse noise removal method based on a 1-D SMF and a space-filling curve which is adaptively drawn using a minimum spanning tree reflecting structural context of an Input Image.
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IECON - Impulse noise removal by using one-dimensional switching median filter applied along space-filling curve reflecting structural context of Image
IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013Co-Authors: Takanori Koga, Noriaki Suetake, Tsuyoshi Kato, Eiji UchinoAbstract:A switching median filter (SMF) is effective for impulse noise removal while still preserving edges in an Input Image. This filter firstly detects pixels corrupted by impulse noise and then filters only the noise-corrupted pixels. However the noise detection process does not always work perfectly. Particularly, pixels constituting thin lines in an Input Image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered despite the needlessness of the filtering. As the result of the filtering, the Image might be over-smoothed and be deteriorated throughout the entire Image. To cope with this problem, we propose a new impulse noise removal method based on a one-dimensional SMF and a space-filling curve which reflects structural contexts of an Input Image. The effectiveness of the proposed method is verified by some experiments.
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Impulse noise removal by using one-dimensional switching median filter applied along space-filling curve reflecting structural context of Image
IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013Co-Authors: Takanori Koga, Noriaki Suetake, Tsuyoshi Kato, Eiji UchinoAbstract:A switching median filter (SMF) is effective for impulse noise removal while still preserving edges in an Input Image. This filter firstly detects pixels corrupted by impulse noise and then filters only the noise-corrupted pixels. However the noise detection process does not always work perfectly. Particularly, pixels constituting thin lines in an Input Image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered despite the needlessness of the filtering. As the result of the filtering, the Image might be over-smoothed and be deteriorated throughout the entire Image. To cope with this problem, we propose a new impulse noise removal method based on a one-dimensional SMF and a space-filling curve which reflects structural contexts of an Input Image. The effectiveness of the proposed method is verified by some experiments.
Noriaki Suetake - One of the best experts on this subject based on the ideXlab platform.
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Impulse noise removal using 1-D switching median filter with adaptive scanning order based on structural context of Image
Optical Review, 2015Co-Authors: Takanori Koga, Noriaki SuetakeAbstract:This paper describes the detail-preserving impulse noise removal performance of a one-dimensional (1-D) switching median filter (SMF) applied along an adaptive space-filling curve. Usually, a SMF with a two-dimensional (2-D) filter window is widely used for impulse noise removal while still preserving detailed parts in an Input Image. However, the noise detector of the 2-D filter does not always distinguish between the original pixels and the noise-corrupted ones perfectly. In particular, pixels constituting thin lines in an Input Image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered regardless of the necessity of the filtering. To cope with this problem, we propose a new impulse noise removal method based on a 1-D SMF and a space-filling curve which is adaptively drawn using a minimum spanning tree reflecting structural context of an Input Image.
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IECON - Impulse noise removal by using one-dimensional switching median filter applied along space-filling curve reflecting structural context of Image
IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013Co-Authors: Takanori Koga, Noriaki Suetake, Tsuyoshi Kato, Eiji UchinoAbstract:A switching median filter (SMF) is effective for impulse noise removal while still preserving edges in an Input Image. This filter firstly detects pixels corrupted by impulse noise and then filters only the noise-corrupted pixels. However the noise detection process does not always work perfectly. Particularly, pixels constituting thin lines in an Input Image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered despite the needlessness of the filtering. As the result of the filtering, the Image might be over-smoothed and be deteriorated throughout the entire Image. To cope with this problem, we propose a new impulse noise removal method based on a one-dimensional SMF and a space-filling curve which reflects structural contexts of an Input Image. The effectiveness of the proposed method is verified by some experiments.
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Impulse noise removal by using one-dimensional switching median filter applied along space-filling curve reflecting structural context of Image
IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013Co-Authors: Takanori Koga, Noriaki Suetake, Tsuyoshi Kato, Eiji UchinoAbstract:A switching median filter (SMF) is effective for impulse noise removal while still preserving edges in an Input Image. This filter firstly detects pixels corrupted by impulse noise and then filters only the noise-corrupted pixels. However the noise detection process does not always work perfectly. Particularly, pixels constituting thin lines in an Input Image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered despite the needlessness of the filtering. As the result of the filtering, the Image might be over-smoothed and be deteriorated throughout the entire Image. To cope with this problem, we propose a new impulse noise removal method based on a one-dimensional SMF and a space-filling curve which reflects structural contexts of an Input Image. The effectiveness of the proposed method is verified by some experiments.
Yannan Li - One of the best experts on this subject based on the ideXlab platform.
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A method of single reference Image based scene relighting.
MethodsX, 2018Co-Authors: Yannan Li, Ri Xu, Xiaokun Zhang, Xiaodong LiAbstract:Abstract Image relighting is to change the illumination of an Image to a target illumination effect without known the original scene geometry, material information and illumination condition. We propose a novel outdoor scene relighting method, which needs only a single reference Image and is based on material constrained layer decomposition. Firstly, the material map is extracted from the Input Image. Then, the reference Image is warped to the Input Image through patch match based Image warping. Lastly, the Input Image is relit using material constrained layer decomposition. The experimental results reveal that our method can produce similar illumination effect as that of the reference Image on the Input Image using only a single reference Image.
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Scene Relighting Using a Single Reference Image Through Material Constrained Layer Decomposition
Studies in Computational Intelligence, 2017Co-Authors: Yannan Li, Xiaodong Li, Quan Zhou, Yulu Tian, Shiming GeAbstract:Image relighting is to change the illumination of an Image to a target illumination effect without known the original scene geometry, material information and illumination condition. We propose a novel outdoor scene relighting method, which needs only a single reference Image and is based on material constrained layer decomposition. Firstly, the material map is extracted from the Input Image. Then, the reference Image is warped to the Input Image through patch match based Image warping. Lastly, the Input Image is relighted using material constrained layer decomposition. The experimental results reveal that our method can produce similar illumination effect as that of the reference Image on the Input Image using only a single reference Image.
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Single Reference Image based Scene Relighting via Material Guided Filtering
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Yannan Li, Xiaodong Li, Xianggang Jiang, Chaoen Xiao, Shiming GeAbstract:Image relighting is to change the illumination of an Image to a target illumination effect without known the original scene geometry, material information and illumination condition. We propose a novel outdoor scene relighting method, which needs only a single reference Image and is based on material constrained layer decomposition. Firstly, the material map is extracted from the Input Image. Then, the reference Image is warped to the Input Image through patch match based Image warping. Lastly, the Input Image is relit using material constrained layer decomposition. The experimental results reveal that our method can produce similar illumination effect as that of the reference Image on the Input Image using only a single reference Image.
Xiaodong Li - One of the best experts on this subject based on the ideXlab platform.
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A method of single reference Image based scene relighting.
MethodsX, 2018Co-Authors: Yannan Li, Ri Xu, Xiaokun Zhang, Xiaodong LiAbstract:Abstract Image relighting is to change the illumination of an Image to a target illumination effect without known the original scene geometry, material information and illumination condition. We propose a novel outdoor scene relighting method, which needs only a single reference Image and is based on material constrained layer decomposition. Firstly, the material map is extracted from the Input Image. Then, the reference Image is warped to the Input Image through patch match based Image warping. Lastly, the Input Image is relit using material constrained layer decomposition. The experimental results reveal that our method can produce similar illumination effect as that of the reference Image on the Input Image using only a single reference Image.
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Scene Relighting Using a Single Reference Image Through Material Constrained Layer Decomposition
Studies in Computational Intelligence, 2017Co-Authors: Yannan Li, Xiaodong Li, Quan Zhou, Yulu Tian, Shiming GeAbstract:Image relighting is to change the illumination of an Image to a target illumination effect without known the original scene geometry, material information and illumination condition. We propose a novel outdoor scene relighting method, which needs only a single reference Image and is based on material constrained layer decomposition. Firstly, the material map is extracted from the Input Image. Then, the reference Image is warped to the Input Image through patch match based Image warping. Lastly, the Input Image is relighted using material constrained layer decomposition. The experimental results reveal that our method can produce similar illumination effect as that of the reference Image on the Input Image using only a single reference Image.
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Single Reference Image based Scene Relighting via Material Guided Filtering
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Yannan Li, Xiaodong Li, Xianggang Jiang, Chaoen Xiao, Shiming GeAbstract:Image relighting is to change the illumination of an Image to a target illumination effect without known the original scene geometry, material information and illumination condition. We propose a novel outdoor scene relighting method, which needs only a single reference Image and is based on material constrained layer decomposition. Firstly, the material map is extracted from the Input Image. Then, the reference Image is warped to the Input Image through patch match based Image warping. Lastly, the Input Image is relit using material constrained layer decomposition. The experimental results reveal that our method can produce similar illumination effect as that of the reference Image on the Input Image using only a single reference Image.
