Regularized image restoration algorithm on sparse gradient prior model
稀疏梯度先验模型的正则化图像复原

The traditional Lucy-Richardson algorithm is an iterative image restoration method based on Bayesian analysis. It achieves good results for restoring images degraded with a high signal-to-noise ratio(SNR). The algorithm is so sensitive to noises that some regularized methods are introduced into the LR-algorithm. However, these tricks often tend to produce excessive smoothing. Therefore, in this paper,we introduce the image sparse prior model as a regularization item into the LR-algorithm, and get a new regularization LR algorithm to suppress noise amplification in the iterative process. To be different from the conventional gratitude-restriction approaches, the algorithm proposes a varying parameterized sparse gradient regularization restriction method, which enables the gradient distribution parameters of the restored image more close to the true gradient distribution and avoids excessive smoothing of restored image by adjusting the regular coefficient. The experimental results show that the algorithm can efficiently suppress the amplification of noises and preserve the details of images.