TY - GEN
T1 - Hyperspectral image denoising from an incomplete observation
AU - Wei, Wei
AU - Zhang, Lei
AU - Zhang, Yanning
AU - Wang, Cong
AU - Tian, Chunna
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/6/22
Y1 - 2016/6/22
N2 - Hyperspectral image (HSI) contains rich spectral information, which can facilitate lots of vision based tasks related with immersive communications. However, HSI is easily affected by different factors such as noise, missing data, etc., which degrades the image quality of HSI and makes HSI incomplete. In this study, to guarantee the denoising method can be used for incomplete data and suppress multiple kinds of noise, we analyze HSI denoising as a low-rank matrix analysis (LRMA) problem taking advantage of Hyperspectral unmixing, and model LRMA for HSI denoising probabilistically. A Bayesian LRMA method is then introduced to solve the probabilistic LRMA problem. The proposed method can denoise the noisy incomplete HSI more effectively compared with several denoising methods. Experimental results demonstrate the effectiveness of the proposed method.
AB - Hyperspectral image (HSI) contains rich spectral information, which can facilitate lots of vision based tasks related with immersive communications. However, HSI is easily affected by different factors such as noise, missing data, etc., which degrades the image quality of HSI and makes HSI incomplete. In this study, to guarantee the denoising method can be used for incomplete data and suppress multiple kinds of noise, we analyze HSI denoising as a low-rank matrix analysis (LRMA) problem taking advantage of Hyperspectral unmixing, and model LRMA for HSI denoising probabilistically. A Bayesian LRMA method is then introduced to solve the probabilistic LRMA problem. The proposed method can denoise the noisy incomplete HSI more effectively compared with several denoising methods. Experimental results demonstrate the effectiveness of the proposed method.
KW - hyperspectral image
KW - image denoising
KW - low rank matrix analysis
UR - https://www.scopus.com/pages/publications/84980385727
U2 - 10.1109/ICOT.2015.7498517
DO - 10.1109/ICOT.2015.7498517
M3 - 会议稿件
AN - SCOPUS:84980385727
T3 - Proceedings of 2015 International Conference on Orange Technologies, ICOT 2015
SP - 177
EP - 180
BT - Proceedings of 2015 International Conference on Orange Technologies, ICOT 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Orange Technologies, ICOT 2015
Y2 - 19 December 2015 through 22 December 2015
ER -