针对快速运动形成的图像模糊，提出了一种运动模糊图像盲解卷积算法。首先，对被噪声污染的频谱图像进行脊波增强; 然后，采用一种新的基于Radon变换的鲁棒算法来确定模糊核函数，该算法在小模糊长度和低信噪比的条件下仍能准确地估计模糊核参数; 确定模糊核函数后，采用基于hyper-laplacian先验的快速非盲解卷积算法来恢复模糊图像。实验结果证明，与基于机器学习的R.Fergus的算法相比较，本文算法在获得相近效果的前提下，计算时间从近30 min下降到40 s左右。该算法对合成运动模糊图像和实际相机运动的自然模糊图像都具有较好的恢复效果。

For a blurred image caused by fast movement, a fast blind deconvolution algorithm for spatially-invariant motion blurred images was proposed. Firstly, the ridge wave of a frequency spectral image with noises was enhanced. Then a robust algorithm based on Ridgelet transform and Radon transform was used to estimate blur kernels in the frequency domain, by which the lengths and directions of motion blur kernels could be accurately estimated, even for small length parameters and blur images in low SNRs .Furthermore, a fast non-blind deconvolution method based on hyper-laplacian prior was used to restore blur images. Experimental results show that the proposed method can restore a 1 megapixel image in less 40 s. As compared with R. Fergus′ algorithm based on machine learning, the proposed algorithm reduces the computing time from 30 min to 40 s while keeps the comparable quality. Moreover, the algorithm is effective not only for the artificially blurred images, but also for the naturally blurred images (by camera movement) as well.