在基于小波变换的图像去噪中，广义交叉验证算法利用统计方式能够获得对小波去噪阈值的最优估计，因此得到广泛应用。但是广义交叉验证具有较高计算复杂度，对图幅较大的高空间分辨率遥感图像，计算去噪阈值所消耗的时间较长。提出一种基于感兴趣区与快速广义交叉验证的高分辨率遥感图像去噪算法。新算法利用形状自适应整数小波变换来提取图像感兴趣区，通过快速广义交叉验证计算高分辨率遥感图像感兴趣区域的小波去噪阈值，最后利用软阈值算法完成感兴趣区去噪。实验表明，新算法不仅能够实现遥感图像感兴趣区域的优先去噪，而且有效降低了广义交叉验证的计算复杂度，对今后的高空间分辨率遥感图像去噪具有一定价值。

In the image denoising methods based on discrete wavelet transform, the generalized cross validation (GCV) algorithm has been proven to be an effective statistical way for estimating the optimal threshold and used widely to remove the image noise. However, GCV has the higher computational complexity than other denoising threshold estimating method. For the high spatial resolution remote sensing image, the GCV algorithm spends most time for computing the wavelet denoising threshold of every subband. An effective and efficient high spatial resolution remote sensing image denosing algorithm based on region of interest (ROI) and fast GCV is proposed. This new algorithm first obtains these image regions of interest (ROI) using shape adaptive integer wavelet transform (SA-IWT) and then computes the denoising threshold of ROI on the high spatial resolution remote sensing image by fast GCV algorithm. Finally, the new algorithm completes the ROI denoising using the soft-threshold merhod. The experimental results show that the new algorithm can not only first complete ROI denoising of the remote sensing image, but also reduce the computational complexity of GCV effectively. This new method is valuable for future high spatial resolution remote sensing image denoising.