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| High resolution remote sensing image denoising based on Curvelet-Wavelet transform |
| WEN Nu1,2,3,YANG Shi-zhi1,2,CUI Sheng-cheng1,2 |
| 1.Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; 2. Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences, Hefei 230031, China; 3.University of Chinese Academy of Sciences, Beijing 100049, China |
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Abstract A novel donoising method in multi-domain based on cartoon and texture decomposition model was proposed to remove the additional noise from remote sensing image. The algorithm considered that Curvelet transform and Wavelet transform have different sparse representation features for different image parts. An undecimated version of orthogonal Wavelet and a wrapping-based Curvelet transform were used. Curvelet coefficients and Wavelet coefficients were constructed by using image decomposition model, and Gaussian scale mixtures (GSM) model was combined to remove image noise. Experimental results show that the method can effectively preserve image details and edges as well as remove pseudo-Gibbs phenomena, and the peak signal-to-noise ratio evidently increases.
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Published: 06 June 2018
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基于Curvelet-Wavelet变换高分辨率遥感图像降噪
基于图像卡通-纹理分解模型,利用Curvelet变换和Wavelet变换对图像不同部分具有不同的稀疏表示特性,提出新的混合域遥感图像降噪方法.利用分解模型对图像分别进行Curvelet域和Wavelet域中的系数建模,结合高斯混合尺度模型（GSM）对图像卡通部分和纹理部分进行降噪,之后对图像进行合并.实验结果表明,该方法降噪后图像峰值信噪比（PSNR）明显提高,有效地保持了图像的细节和边缘,抑制了降噪图像的混叠现象.
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