基于单一全变分正则的多通道图像盲复原算法容易使复原图像产生振铃效应、丢失高频细节信息。针对这个问题,利用模糊图像暗像素的非稀疏性,提出一种基于全变分和暗像素的多通道图像盲复原算法。针对全变分和暗像素双正则模型求解难的问题,使用分裂Bregman优化算法确保结果收敛,将全局问题分解为独立的子问题,通过交替迭代图像和点扩展函数复原出目标图像。实验结果表明,所提算法能够有效去除图像模糊,抑制振铃效应,复原出高质量的清晰图像。与采用单一全变分正则项的算法相比,所提算法的峰值信噪比提高了0.12 dB~5.86 dB,结构相似度提高了0.014~0.125。

A multi-channel image blind restoration algorithm based on single total variation regularity can cause a ringing effect and loss high-frequency information in restored images. To solve this problem, a multi-channel image blind restoration algorithm based on total variation and dark pixels is proposed using the non-sparseness of dark pixels in blurred images. Solving the problem of total variation and dark pixel double regularization model is difficult. To address the difficult problem, the split Bregman optimization algorithm is used to ensure convergence of the results, the global problem is decomposed into independent sub-problems, and the image and point spread function are solved alternately to restore the target images. The experimental results demonstrate that the proposed algorithm can effectively remove image blurring, suppress ringing effects, and restore high-quality clear images. Compared to an algorithm with the total variation regular term, the peak signal-to-noise ratio of the proposed algorithm improves by 0.12 dB--5.86 dB, and the structural similarity improves by 0.014--0.125.