基于布雷格曼迭代的稀疏正则化图像复原方法

A Bregman Iteration Sparsity Regularization Method for Image Restoration

为了实现模糊噪声图像的清晰化复原，提出了一种基于布雷格曼迭代的稀疏正则化约束的图像复原算法。首先，运用差分算子，得到图像中各个方向上的梯度信息；然后，利用提取的梯度信息，得到图像边缘各个方向上的权重；并结合稀疏性原理，针对复原图像，提出了一种权重的稀疏性正则化约束；最后，运用了一种布雷格曼迭代（Bregman Iteration，BI）策略对提出的方法进行最优化求解。实验结果表明，较近几年的一些具有代表性的图像复原方法相比，不仅主观的视觉效果得到了较为明显的改进，而且客观的信噪比增量也增加了0．3～2．5dB。

In order to recover the blurred-noisy image, a Bregman-iteration based weighted sparsity regulariza- tion method for image restoration is proposed. First, using the difference operator, the gradient information of dif- ferent directions in the image can be obtained. Second, making use of the gradient information, the weights for im- age edges in different directions can be obtained. Then, combining the sparsity theory, a weighted sparsity regulari- zation constraint is proposed. Finally, a Bregman iteration （BI） approach is employed to restore the image. Experi- mental results indicate that the proposed method outperforms some representative image restoration methods, not on- ly the subjective vision has the betterment obviously proves 2.2 dB. , but also the increase of the signal to noise ratio （ISNR） im-