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Titlebook: Combating Bad Weather Part II; Fog Removal from Ima Sudipta Mukhopadhyay,Abhishek Kumar Tripathi Book 2015 Springer Nature Switzerland AG 2

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發(fā)表于 2025-3-21 19:06:48 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Combating Bad Weather Part II
副標(biāo)題Fog Removal from Ima
編輯Sudipta Mukhopadhyay,Abhishek Kumar Tripathi
視頻videohttp://file.papertrans.cn/230/229834/229834.mp4
叢書名稱Synthesis Lectures on Image, Video, and Multimedia Processing
圖書封面Titlebook: Combating Bad Weather Part II; Fog Removal from Ima Sudipta Mukhopadhyay,Abhishek Kumar Tripathi Book 2015 Springer Nature Switzerland AG 2
描述Every year lives and properties are lost in road accidents. About one-fourth of these accidents are due to low vision in foggy weather. At present, there is no algorithm that is specifically designed for the removal of fog from videos. Application of a single-image fog removal algorithm over each video frame is a time-consuming and costly affair. It is demonstrated that with the intelligent use of temporal redundancy, fog removal algorithms designed for a single image can be extended to the real-time video application. Results confirm that the presented framework used for the extension of the fog removal algorithms for images to videos can reduce the complexity to a great extent with no loss of perceptual quality. This paves the way for the real-life application of the video fog removal algorithm. In order to remove fog, an efficient fog removal algorithm using anisotropic diffusion is developed. The presented fog removal algorithm uses new dark channel assumption and anisotropic diffusion for the initialization and refinement of the airlight map, respectively. Use of anisotropic diffusion helps to estimate the better airlight map estimation. The said fog removal algorithm requires
出版日期Book 2015
版次1
doihttps://doi.org/10.1007/978-3-031-02252-4
isbn_softcover978-3-031-01124-5
isbn_ebook978-3-031-02252-4Series ISSN 1559-8136 Series E-ISSN 1559-8144
issn_series 1559-8136
copyrightSpringer Nature Switzerland AG 2015
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 20:55:53 | 只看該作者
https://doi.org/10.1057/9780230512795algorithm outperforms prior state of the art algorithms in terms of contrast gain, percentage of the number of saturated pixels and computation time. The presented algorithm is independent of the density of the fog and does not require user intervention. It can handle color as well as gray images. A
板凳
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地板
發(fā)表于 2025-3-22 07:31:42 | 只看該作者
Conclusions and Future Directions,algorithm outperforms prior state of the art algorithms in terms of contrast gain, percentage of the number of saturated pixels and computation time. The presented algorithm is independent of the density of the fog and does not require user intervention. It can handle color as well as gray images. A
5#
發(fā)表于 2025-3-22 09:48:00 | 只看該作者
Book 2015eveloped. The presented fog removal algorithm uses new dark channel assumption and anisotropic diffusion for the initialization and refinement of the airlight map, respectively. Use of anisotropic diffusion helps to estimate the better airlight map estimation. The said fog removal algorithm requires
6#
發(fā)表于 2025-3-22 16:26:07 | 只看該作者
Single-Image Fog Removal Using an Anisotropic Diffusion,
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發(fā)表于 2025-3-22 23:55:08 | 只看該作者
1559-8136 nt of the airlight map, respectively. Use of anisotropic diffusion helps to estimate the better airlight map estimation. The said fog removal algorithm requires978-3-031-01124-5978-3-031-02252-4Series ISSN 1559-8136 Series E-ISSN 1559-8144
9#
發(fā)表于 2025-3-23 04:24:16 | 只看該作者
1559-8136 resent, there is no algorithm that is specifically designed for the removal of fog from videos. Application of a single-image fog removal algorithm over each video frame is a time-consuming and costly affair. It is demonstrated that with the intelligent use of temporal redundancy, fog removal algori
10#
發(fā)表于 2025-3-23 06:03:50 | 只看該作者
Book 2015ere is no algorithm that is specifically designed for the removal of fog from videos. Application of a single-image fog removal algorithm over each video frame is a time-consuming and costly affair. It is demonstrated that with the intelligent use of temporal redundancy, fog removal algorithms desig
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