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针对大部分已有的遥感图像去噪算法在去噪的同时不能有效的保留细节和增强边缘,提出了一种基于Cycle Spinning Contourlet变换和总变分最小化的图像去噪新算法.该算法依据了Cycle Spinning Contourlet变换能够很好的保留原始图像的细节和纹理信息,而总变分最小化方法具有在去噪的同时增强图像边缘的特性,因此使用所提出的融合规则对两种算法去噪后的图像进行融合能够取得更好的增强效果.通过对比,实验结果表明该算法不仅能在很大程度上削弱分别由平移不变Contourlet变换和总变分最小化的图像去噪方法产生的伪吉布斯现象和阶梯效应,而且视觉效果和PSNR值均优于其它方法,同时该算法能够保留更多的光谱信息,因此该算法是一种有效的遥感图像去噪算法.
Aiming at the problem that most of the existing remote sensing image denoising algorithms can not effectively preserve the details and enhance the edges while denoising, a new algorithm for image denoising based on Cycle Spinning Contourlet transform and total variation minimization is proposed. The algorithm is based on The Cycle Spinning Contourlet transform preserves the detail and texture information of the original image well. However, the total variation minimization method has the property of denoising and enhancing the edge of the image. Therefore, the proposed fusion rules denoise the two algorithms The results show that the proposed algorithm can not only weaken the pseudo-artifacts of the image denoising method with the translation invariant Contourlet transform and the total variation respectively, Gibbs phenomenon and staircase effect, and the visual effect and PSNR value are superior to other methods. At the same time, the algorithm can retain more spectral information, so this algorithm is an effective remote sensing image denoising algorithm.