Laser & Optoelectronics Progress, Volume. 55, Issue 5, 053004(2018)
Spectral Reflectance Reconstruction Based on Dimension Reduction Regularization Polynomials
Fig. 3. Relationship between training sample number and mean RMSE of reconstructed spectral reflectance
Fig. 4. Regularization parameters obtained by L-curve method. (a) Training result 1; (b) training result 2
Fig. 6. Spectral reflectance curves with three reconstruction methods. (a) xISSD=0.022; (b) xISSD=0.121; (c) xISSD=0.351
Fig. 7. Mural referential color patches and multispectral images. (a) Markings of mural referential color patches; (b) multispectral images with 11 channels
Fig. 8. CIELAB chromaticity distribution space of mural referential color patches obtained by different reconstruction methods
Fig. 9. Reconstructed and measured spectral reflectance curves of six referential color patches of mural. (a) Color patch 1; (b) color patch 2; (c) color patch 3; (d) color patch 4; (e) color patch 5; (f) color patch 6
|
|
|
|
Get Citation
Copy Citation Text
Ke Wang, Huiqin Wang, Yanqun Long, Weichao Wang, Lijuan Zhao, Lei Yang. Spectral Reflectance Reconstruction Based on Dimension Reduction Regularization Polynomials[J]. Laser & Optoelectronics Progress, 2018, 55(5): 053004
Category: Spectroscopy
Received: Oct. 20, 2017
Accepted: --
Published Online: Sep. 11, 2018
The Author Email: Wang Ke (wangke@xauat.edu.cn)