Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (5): 21-27.doi: 10.16180/j.cnki.issn1007-7820.2020.05.004

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Hyperspectral Image Denoising via Spectral Weighted Low-rank Matrix Approximation

LIU Lu,ZHANG Hongyan,ZHANG Liangpei   

  1. State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University, Wuhan 430079, China
  • Received:2019-03-09 Online:2020-05-15 Published:2020-06-02
  • Supported by:
    Natural Science Foundation of China(61871298);Natural Science Foundation of China(41571362);CRSRI Open Research Program(CKWV2016388/KY)


HSIs are often contaminated by various types of noise, which degrades the quality of the acquired image and limits the subsequent application. Furthermore, the noise of HSI appears different statics and intensity in different bands. In this paper, a spectral weighted low-rank approximation model was proposed for hyperspectral image denoising. The spectral weighted matrix was introduced to balance the data fidelity of the different bands in consideration of their different noise intensity. To further separate the noise from the clean image, weighted nuclear norm minimization was utilized to depict the patch-wise low-rank structure of the high dimensional HSI. The proposed model was formulated into a linear equality-constrained problem and solved by alternating direction method of multipliers. Experimental results on both simulated and real HSI datasets validated the effectiveness and superiority of the proposed method.

Key words: remote sensing, hyperspectral image denoising, spectral weighted matrix, low-rank matrix approximation, weighted nuclear norm, alternating direction method of multipliers

CLC Number: 

  • TP751