TY - GEN
T1 - Hyperspectral image denoising with segmentation-based low rank representation
AU - Ma, Jiayi
AU - Jiang, Junjun
AU - Li, Chang
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/4
Y1 - 2017/1/4
N2 - Recently, low-rank representation (LRR) based hyperspectral image (HSI) denoising method has been proven to be a powerful tool for removing different kinds of noise simultaneously, such as Gaussian, dead pixels and impulse noise. However, the LRR based method cannot make full use of the spatial information in HSI. In this paper, we integrate the graph based segmentation (GS) into the LRR, and propose a novel denoising method named GS-LRR. We first use the principle component analysis (PCA) to obtain the first principle component of HSI. Then the graph based segmentation is adopted to the first principle component of HSI to get homogeneous regions. Finally, we employ the LRR to each homogeneous region of HSI, which enable us to simultaneously remove all the above mentioned mixed noise. Extensive experiments on both simulated and real HSIs demonstrate the efficiency of the proposed GS-LRR.
AB - Recently, low-rank representation (LRR) based hyperspectral image (HSI) denoising method has been proven to be a powerful tool for removing different kinds of noise simultaneously, such as Gaussian, dead pixels and impulse noise. However, the LRR based method cannot make full use of the spatial information in HSI. In this paper, we integrate the graph based segmentation (GS) into the LRR, and propose a novel denoising method named GS-LRR. We first use the principle component analysis (PCA) to obtain the first principle component of HSI. Then the graph based segmentation is adopted to the first principle component of HSI to get homogeneous regions. Finally, we employ the LRR to each homogeneous region of HSI, which enable us to simultaneously remove all the above mentioned mixed noise. Extensive experiments on both simulated and real HSIs demonstrate the efficiency of the proposed GS-LRR.
KW - Denoising
KW - Mixed noise
KW - graph based segmentation
KW - hyper-spectral image
KW - low-rank representation
UR - https://www.scopus.com/pages/publications/85011031789
U2 - 10.1109/VCIP.2016.7805490
DO - 10.1109/VCIP.2016.7805490
M3 - 会议稿件
AN - SCOPUS:85011031789
T3 - VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing
BT - VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Visual Communication and Image Processing, VCIP 2016
Y2 - 27 November 2016 through 30 November 2016
ER -