多信道图像盲复原算法

为充分利用观测图像中的信息及信息之间的互补性，提高图像复原的质量，提出了贝叶斯框架下的多信道图像盲复原算法。首先，建立原始图像、点扩散函数和观测图像的先验模型，由先验模型得到原始图像、点扩散函数和观测图像的先验概率;然后用伽马分布描述未知的模型参数;最后基于最大后验概率的推导，利用实证分析法估计原始图像和点扩散函数的最优值。实验结果表明，相比单信道的图像盲复原算法，多信道的图像盲复原算法可以得到更好的复原结果。同时，与某些经典的多信道图像盲复原算法相比，提出的算法在复原效果方面具有一定的优势。

Multichannel Blind Image Restoration

In order to well utilize the information in the observed images and the complementarities between the information to improve the results of the image restoration, this paper proposed a blind restoration algorithm for multichannel images. Firstly, the prior models of the original image, point spread functions and the observed images were reconstructed, from which the prior distributions of them were obtained; secondly, the Uamma distribution was used to describe the unknown model parameters; finally, based on the inference of the max posterior probability, the optimal original image and the point spread functions were estimated using the evidence analysis method. Compared with the single channel algorithms, the experiments show that the multichannel blind image restoration can obtain better results. Meanwhile,the proposed algorithm shows competitive performance on restored results compared with some state-of-the-art algorithms.