Abstract
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.
Highlights
Hyperspectral remote sensing image acquision is a complicated process, in which image noise is generated unavoidably
Chen et al [6] proposed a new hyperspectral image denoising algorithm by adding a PCA transform before using wavelet shrinkage; first, a PCA transform was implemented on the original hyperspectral image, and the low-energy PCA output channel was denoised with wavelet shrinkage denoising processes
Based on the maximum a posteriori (MAP) estimation theory, the denoising model for a hyperspectral image can be represented as the following constrained least squares problem: u arg min g u 2 R u where g represents the observed hyperspectral image, the term g u 2 represents the data fidelity between the 2 observed noisy image and the original clean image, and R(u) is the regularization item, which gives a prior model of the original clear hyperspectral image. is the regularization parameter which controls the relative contribution of data fidelity term g u 2 and regularization term R u . 2
Summary
Hyperspectral remote sensing image acquision is a complicated process, in which image noise is generated unavoidably. A tenser-filter-based hyperspectral image denoising algorithm was proposed by Salah Bourennane et al [4]. Chen et al [6] proposed a new hyperspectral image denoising algorithm by adding a PCA transform before using wavelet shrinkage; first, a PCA transform was implemented on the original hyperspectral image, and the low-energy PCA output channel was denoised with wavelet shrinkage denoising processes. Most of these denoising algorithms deal with hyperspectral image band by band without considering hyperspectral image cube as a whole integrity. This paper proposes a cubic total variation (CTV) model based hyperspectral image denoising method to treat the hyperspectal image as a whole 3-D integrity from both the spatial and spectral dimension
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