HYPERSPECTRAL IMAGE DENOISING WITH CUBIC TOTAL VARIATION MODEL
HYPERSPECTRAL IMAGE DENOISING WITH CUBIC TOTAL VARIATION MODEL
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DOI：
10.5194/isprsannals-i-7-95-2012
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发表时间：
2012-07
期刊：
影响因子：
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通讯作者：
Hongyan Zhang
中科院分区：
文献类型：
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作者：
Hongyan Zhang
抽象的。高光谱图像采集过程中会不可避免地产生图像噪声，对后续的图像分析产生负面影响。因此，有必要对高光谱图像进行去噪处理。将二维空间域全变分模型与一维谱域全变分模型相结合，提出了一种三次全变分（CTV）模型，并将其应用于高光谱图像去噪。利用增广拉格朗日方法来提高所需的高光谱图像的解算速度。实验结果表明，该方法可以实现有竞争力的图像质量。
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.