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为了提高基于学习的维纳滤波方法性能，将广义典型相关分析理论推广到核空间中，并在核空间中将此方法与维纳滤波相结合，提出了基于核广义典型相关分析的维纳滤波及其快速算法。该方法首先使用主成分分析对噪声图像进行预处理，而后将处理后图像数据映射到高维核空间中，使用核技巧依据核广义典型相关分析理论抽取相关特征来计算维纳滤波所需的降秩估计量，最后利用维纳滤波的均方误差最小的理念获取线性空间内的图像恢复结果。为了减少在特征抽取过程中的计算量，以空间变换的方法减少了矩阵维数；为了进一步提升图像恢复效果，在维纳滤波中引入了保真项。实验表明，该方法所抽取的相关特征能够降低图像恢复结果的错误率，并且该恢复过程对于降秩估计量秩的大小以及算法迭代次数具有健壮性，快速算法能够在保持图像质量的情况下减少25%以上的时间消耗。“,”To improve the performance of wiener filter based on learning, this paper extends the Generalized Canonical Correlation Analysis (GCCA) from the linear space to the nonlinear kernel space and addresses the kernel wiener filter based on kernel GCCA and its corresponding fast algorithm. To acquire the restored images, the Principal Components Analysis is firstly used to process the noisy images. Secondly, the processed image data sets are transformed to the high dimensional space. Then, with the kernel trick and the features extracted by Kernel Generalized Canonical Correlation Analysis, the reduced rank estimator needed by kernel wiener filter is calculated. After that, the kernel wiener filter which solves problems with the method of minimizing the mean square error is used to acquire the restored images in the original space. To reduce the computation during the feature extraction, a space transform method is used to reduce the dimension of matrices. To improve the effect of the restored image, the fidelity term is added into the wiener filter. The experiment demonstrates that the features extracted by the new method are able to reduce the error rate of the restored images and the new method is robust to the rank of the reduced rank estimator and the iterative times of the arithmetic. Additionally, the fast algorithm can reduce the time consuming at least 25% while preserving the quality of restored image.