A Rational Function Model Refining Method Using Compressive Sampling
Authors: 
Pu Wang, Wei An, Jing GuoAuthors Info & Claims
Pu Wang, Wei An, Jing GuoAuthors Info & ClaimsPages 81 - 85
Abstract
The rational function model (RFM) is a widely used imaging geometry model in the geometric correction of remote sensing image. The RFM refining methods can improve the performance of RFM by refining RFM coefficients(RCPs) or compensating the residual errors caused by the inaccuracy of RCPs. However, it's difficult for traditional RFM refining methods to correct high frequency errors. This paper presents a new RFM refining method using compressive sampling to operate for high frequency distortion, called RFM-CS. This method is based on the idea that the residual errors of images can be regarded as 2-dimensional signals which are compressible. By using the technique of compressive sampling, the residual errors of the whole image can be reconstructed and compensated. The experiment results show that RFM-CS outperforms traditional RFM refining methods when there are high frequency errors.
References
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Index Terms
- A Rational Function Model Refining Method Using Compressive Sampling
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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
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 July 2014
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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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