Single Image Super-resolution Reconstruction with Neural Network and Gaussian Process Regression
Pages 386 - 390
Abstract
In this paper, we propose a novel single image super-resolution (SR) reconstruction framework based on artificial neural network (ANN) and Gaussian process regression (GPR). The ANN is used for SR reconstruction, and the GPR is used for correction. The new framework combines multiple reconstruction approaches including deep learning and sparse representation from a local dictionary. The main contribution is enhancing the reconstruction performance utilizing the image compressed features with respect to other state of art single image SR approaches in terms of both visual perception and quantitative assessment.
References
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Index Terms
- Single Image Super-resolution Reconstruction with Neural Network and Gaussian Process Regression
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Information & Contributors
Information
Published In
July 2014
430 pages
ISBN:9781450328104
DOI:10.1145/2632856
- General Chairs:
- Hanzi Wang,
- Larry Davis,
- Program Chairs:
- Wenwu Zhu,
- Stephan Kopf,
- Yanyun Qu,
- Publications Chairs:
- Jun Yu,
- Jitao Sang,
- Tao Mei
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In-Cooperation
- NSF of China: National Natural Science Foundation of China
- Beijing ACM SIGMM Chapter
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 July 2014
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- Research
- Refereed limited
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ICIMCS '14
ICIMCS '14: International Conference on Internet Multimedia Computing and Service
July 10 - 12, 2014
Xiamen, China
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Overall Acceptance Rate 163 of 456 submissions, 36%
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