Volume I-7
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 95-98, 2012
https://doi.org/10.5194/isprsannals-I-7-95-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 95-98, 2012
https://doi.org/10.5194/isprsannals-I-7-95-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  17 Jul 2012

17 Jul 2012

HYPERSPECTRAL IMAGE DENOISING WITH CUBIC TOTAL VARIATION MODEL

H. Zhang H. Zhang
  • The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China

Keywords: Hyperspectral Image, Denoising, Cubic Total Variation, Augmented Lagrangian Method

Abstract. Image noise is generated unavoidably in the hyperspectral image acquision process and has a negative effect on subsequent image analysis. Therefore, it is necessary to perform image denoising for hyperspectral images. This paper proposes a cubic total variation (CTV) model by combining the 2-D total variation model for spatial domain with the 1-D total variation model for spectral domain, and then applies the termed CTV model to hyperspectral image denoising. The augmented Lagrangian method is utilized to improve the speed of solution of the desired hyperspectral image. The experimental results suggest that the proposed method can achieve competitive image quality.