基于Huber正则化二阶加速Richardson-Lucy湍流图像复原算法

Turbulence-degraded image restoration method using the second-order accelerated Richardson-Lucy algorithm based on Huber regularization

为了快速准确地恢复湍流退化图像,提出了Huber正则化Richardson-Lucy(R-L)加速迭代盲反卷积(IBD)算法。根据图像滤波处理结果,采用Huber函数自适应地选择一阶范数和二阶范数正则化约束,增加算法收敛速度同时提高图像细节和边界复原质量。引入基于泰勒级数的二阶矢量外推加速方法,进一步增加迭代的收敛速度。实验结果表明,采用提议的算法需要的迭代次数较少,适用于实时性要求较高的场合,复原图像的主客观质量均有所提高。

In order to restore turbulence-degraded images rapidly and exactly, an accelerated iterative blind deconvolution （IBD） Richardson-Lucy （R-L） algorithm based on Huber regularization is proposed. The Huber function can select L1 and L2 norms adaptively based on the processing result of image filter. In the smooth area, the Huber function becomes the usual L2 least-squares penalty function with rapid convergence characteristics which can remove false edge, and in the edge area, the L1 penalty function which can maintain the detail information and edge. Then the second-order vector extrapolation acceleration technique is used to accelerate convergence rate. The experimental results show that the proposed algorithm has greater convergence rate and better subjective and objective restoration quality.