Gradient Based Edge Preserving Interpolation and Its Application to Super-Resolution
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- Iwamoto Yutaro
- Intelligent Image Processing Labs.
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- Han Xian-Hua
- Intelligent Image Processing Labs.
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- Tateyama Tomoko
- Intelligent Image Processing Labs.
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- Ohashi Motonori
- Intelligent Image Processing Labs.
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- Sasatani So
- Intelligent Image Processing Labs.
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- Chen Yen-Wei
- Intelligent Image Processing Labs.
Bibliographic Information
- Other Title
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- 勾配情報に基づくエッジ保存補間と超解像技術への応用
- コウバイ ジョウホウ ニ モトズク エッジ ホゾン ホカン ト チョウカイゾウ ギジュツ エ ノ オウヨウ
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Abstract
Super-resolution is a process for obtaining high quality and high resolution images from a set of or only one low-resolution image. The most practical one for image super-resolution is reconstruction-based method, which minimizes the difference between observed low-resolution images and the estimation for high resolution image. Therein, interpolation step plays a key role for the estimated high resolution image quality. Usually, the conventional bilinear or bicubic methods are used in the reconstruction-based super-resolution. However, these conventional interpolations generally lead to blurring on edge regions and need more time for convergence in reconstruction-based super-resolution method. Therefore, this paper propose a gradient based edge preserving interpolation method, which can reduce not only artifact noise but also blurring near edge regions in the estimated high resolution image. Furthermore, our proposed interpolation method can also solve large complexity and time-consuming problem in the recently developed New Edge-directed interpolation, which usually can achieve sharp edge in the high resolution reconstructed image. Experiments validate that our proposed interpolation method for image super-resolution is more effective than the conventional interpolation ones.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 131 (11), 1901-1906, 2011
The Institute of Electrical Engineers of Japan
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Details 詳細情報について
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- CRID
- 1390282679583633280
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- NII Article ID
- 10030528072
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 11293412
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed