Name
Abstract | ||
|---|---|---|
Multispectral imaging system is of wide application in relevant fields for its capability in acquiring spectral information of scenes. Its limitation is that, due to the large number of spectral channels, the imaging process can be quite time-consuming when capturing high-resolution (HR) multispectral images. To resolve this limitation, this paper proposes a fast multispectral imaging framework based on the image sensor pixel-binning and spectral unmixing techniques. The framework comprises a fast imaging stage and a computational reconstruction stage. In the imaging stage, only a few spectral images are acquired in HR, while most spectral images are acquired in low resolution (LR). The LR images are captured by applying pixel binning on the image sensor, such that the exposure time can be greatly reduced. In the reconstruction stage, an optimal number of basis spectra are computed and the signal-dependent noise statistics are estimated. Then the unknown HR images are efficiently reconstructed by solving a closed-form cost function that models the spatial and spectral degradations. The effectiveness of the proposed framework is evaluated using real-scene multispectral images. Experimental results validate that, in general, the method outperforms the state of the arts in terms of reconstruction accuracy, with additional $20\\times $ or more improvement in computational efficiency. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TIP.2016.2576401 | IEEE Trans. Image Processing |
Keywords | Field | DocType |
Image reconstruction,Covariance matrices,Multispectral imaging,Cameras,Spatial resolution | Iterative reconstruction,Computer vision,Full spectral imaging,Pattern recognition,Image sensor,Multispectral image,Spectral line,Artificial intelligence,Multispectral pattern recognition,Pixel,Image resolution,Mathematics | Journal |
Volume | Issue | ISSN |
25 | 8 | 1057-7149 |
Citations | PageRank | References |
2 | 0.40 | 28 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi-Wei Pan | 1 | 4 | 0.77 |
| Hui-Liang Shen | 2 | 91 | 10.83 |
| Chunguang Li | 3 | 228 | 16.58 |
| Shu-Jie Chen | 4 | 15 | 1.67 |
| John H. Xin | 5 | 67 | 6.57 |