Name
Title | ||
|---|---|---|
Improved non-negative tensor Tucker decomposition algorithm for interference hyper-spectral image compression. |
Abstract | ||
|---|---|---|
The compression method, first proposed in 2012, is based on the non-negative tensor decomposition for interference hyper-spectral image data. As a tensor is generated by a huge amount of interference hyper-spectral images, the multiplicative update algorithm is made extremely complicated, and even unfeasible. To reduce the computational cost and speed up the convergence, this paper, based on the characteristics of interference hyper-spectral images, develops a new algorithm using different down-sampling factors for different non-negative wavelet sub-band tensors. The experimental results showed that this algorithm could significantly shorten the running time, while maintaining a good compression performance compared with the conventional methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s11432-014-5165-x | SCIENCE CHINA Information Sciences |
Keywords | DocType | Volume |
interference hyper-spectral images, LASIS, three-dimensional lifting wavelet transform, nonnegative tensor decomposition, image compression, 干涉高光谱图像, LASIS, 3维提升小波变换, 非负张量分解, 图像压缩 | Journal | 58 |
Issue | ISSN | Citations |
5 | 1869-1919 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia Wen | 1 | 2 | 2.26 |
| Junsuo Zhao | 2 | 3 | 3.08 |
| Caiwen Ma | 3 | 0 | 1.01 |
| Cailing Wang | 4 | 6 | 2.81 |