应用自解卷积和增量Wiener滤波实现迭代盲图像复原

Realization of iterative blind image restoration by self deconvolution and increment Wiener filter

提出了一种基于自解卷积和增量Wiener滤波的迭代盲图像复原算法(SDIWF-IBD)。将自解卷积点扩散函数估计法应用于迭代盲目反卷积,准确估计了点扩散函数频域;在图像估计时使用增量Wiener滤波,确保算法稳定收敛。为进一步控制算法收敛速度,引入内迭代加速方法,有效减少了算法的外部迭代次数。实验结果表明,复原后图像细节明显增加且失真小,算法快速收敛于较小误差。该算法复原效果良好,收敛快速可控,有利于实时应用。

An iterative blind image restoration algorithm based on Self-deconvolution and Incremental Wiener Filter（SDIWF-IBD） is proposed.The self-deconvolution estimation for a Point Spread Function（PSF） is applied to Iterative Blind Deconvolution（IBD） to estimating exactly the frequency domain of the PSF.The incremental Wiener filter is used in the image estimation of IBD to keep the algorithm convergence steady.To further control the convergency,an in-iterative acceleration is suggested to control the speed of algorithm convergence and reduce total external iteration.Experimental results indicate that more details are recovered in the restoration image with few distortions,and the algorithm is converged to a small error quickly.It concludes that the SDIWF-IBD algorithm has good restoration ability at a fast and controllable convergency speed,and is fit for applications in real-time.