Hyperspectral image denoising from an incomplete observation

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.

[1]  DAVID ZHANG,et al.  A Comparative Study of Palmprint Recognition Algorithms , 2012, CSUR.

[2]  Claudio Carpineto,et al.  A Survey of Automatic Query Expansion in Information Retrieval , 2012, CSUR.

[3]  Lei Zhang,et al.  Robust Principal Component Analysis with Complex Noise , 2014, ICML.

[4]  Zhouyu Fu,et al.  Discriminant Absorption-Feature Learning for Material Classification , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Liangpei Zhang,et al.  Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Tao Lin,et al.  Survey of hyperspectral image denoising methods based on tensor decompositions , 2013, EURASIP J. Adv. Signal Process..

[7]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[8]  Haixian Wang,et al.  Image Denoising Using Trivariate Shrinkage Filter in the Wavelet Domain and Joint Bilateral Filter in the Spatial Domain , 2009, IEEE Transactions on Image Processing.

[9]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[10]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[11]  Dacheng Tao,et al.  GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case , 2011, ICML.

[12]  Aggelos K. Katsaggelos,et al.  Sparse Bayesian Methods for Low-Rank Matrix Estimation , 2011, IEEE Transactions on Signal Processing.

[13]  Liangpei Zhang,et al.  Hyperspectral Image Restoration Using Low-Rank Matrix Recovery , 2014, IEEE Transactions on Geoscience and Remote Sensing.