An image restoration method based on a dual dictionary was presented under the framework of sparse theory,and the choice of overcomplete dictionaries and the implementation of iteration methods were analyzed. Firstly, the degradation and the restoration models in the sparse theory were established, then the dictionary constructed by Haar coefficients was used to sparse the blurred image and shrink the image with Parallel Coordinate Decent (PCD) iteration algorithm to obtain the elementary deblurred image,in which the blur was removed efficiently, but the noise was weighted and added. For removing the weighted noise, the secondary dictionary from an image database was trained to shrink the deblurred image and get the final result. The results shows that the proposed method can restore the motion-blurred image efficiently, remove motion blur and noise and reserve the edge detail in some extents. Finally the two-level sparse optimization model was expanded and a new idea for the image restoration was presented under the sparse framework.%为了消除图像中的运动模糊,提出了一种稀疏理论框架下的双字典稀疏复原方法,并分析了冗余字典的选取和迭代算法的实现.首先,建立了稀疏变换下的退化和复原模型,用Haar系数冗余字典将图像稀疏化,并用PCD阈值迭代算法对模糊图像进行收敛,得到复原图像.由于在有效去除复原图像的模糊的同时噪声在迭代过程中被放大并叠加在图像上,故从清晰图像库中训练了一个冗余字典进行第二次稀疏收敛来去除去模糊中被加权的噪声.实验结果表明,本文的方法对模糊退化图像有很好的复原效果,不仅有效地去除了运动模糊和噪声,并能在一定程度上保留边缘细节.最后拓展了两层稀疏优化模型,为以后在稀疏框架下的图像复原提供了新的思路.
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