Least Square Based Fast Denoising Approach to Hyperspectral Imagery
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Publisher
Springer Singapore
Link
http://link.springer.com/content/pdf/10.1007/978-981-10-3373-5_9
Reference8 articles.
1. Qiangqiang Yuan, Liangpei Zhang: Hyperspectral Image Denoising Employing a Spectral Spatial Adaptive Total Variation Model. IEEE Trans. Geosci. Remote Sens., vol 50, issue 10, pp 3660–3677, 2012.
2. Ting Li, Xiao-Mei Chen, Bo Xue, Qian-Qian Li, Guo-Qiang Ni: A total variation denoising algorithm for hyperspectral data. Proceeding of SPIE, The International Society for Optical Engineering, Nov 2010.
3. Hao Yang, Dongyan Zhang, Wenjiang Huang, Zhongling Gao, Xiaodong Yang, Cunjun Li, Jihua Wang: Application and Evaluation of Wavelet-based Denoising Method in Hyperspectral Imagery Data. Springer Berlin Heidelberg, CCTA 2011, Part IIIFIP AICT 369, pp 461–469, 2012.
4. Nikhila Haridas, Aswathy. C, V. Sowmya, Soman. K.P.: Hyperspectral Image Denoising Using Legendre-Fenchel Transform for Improved Sparsity Based Classification. Springer International Publishing. Advances in Intelligent Systems and Computing, vol 384, pp 521–528. Aug 2015.
5. Xiang-Yang Wang, Zhong-Kai Fu: A wavelet-based image denoising using least squares support vector machine. Engineering Applications of Artificial Intelligence. vol 23, issue 6, Sep 2010.
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