基于剪切波变换和拟合优度检验的遥感图像去噪

Remote Sensing Image Denoising Based on Shearlet Transform and Goodness of Fit Test

针对遥感图像中的高斯白噪声,提出一种基于剪切波变换和拟合优度检验的遥感图像去噪算法.首先,将含噪遥感图像通过剪切波变换多尺度分解得到不同子带,利用剪切波域下高斯白噪声系数的统计关系估计去噪阈值;其次,计算高频子带的拟合优度检验统计量,将统计量与去噪阈值相比较进行去噪;最后,对系数矩阵进行剪切波逆变换重建去噪图像.仿真实验结果表明,该算法能有效去除遥感图像中的高斯噪声,保持图像的边缘纹理信息,并且在不同噪声水平下,均获得了较高的峰值信噪比,其中与剪切波阈值去噪算法相比平均提高0.33 dB.

Aiming at white Gaussian noise in remote sensing images,we proposed a remote sensing images denoising algorithm based on shearlet transform and goodness of fit test.Firstly,the noisy remote sensing image was decomposed into different sub-bands through shearlet transform at multiple scales,and the denoising threshold was estimated using the statistical relationship of white Gaussian noise coefficients in the shearlet domain.Secondly,we calculated the goodness of fit test statistics of high-frequency sub-bands and compared it with the denoising threshold for denoising.Finally,shearlet inverse transform on the coefficient matrix was performed to reconstruct the denoised images.The simulation experiment results show that this algorithm can effectively remove Gaussian noise in remote sensing images,maintain the edge texture information of images,and achieve high peak signal-to-noise ratio under different noise levels,among which the average increase is 0.33 dB compared with the shearlet threshold denoising algorithm.