一种基于正则化方法的准最佳图像复原技术

A Close-to-Optimal Image Restoration Technique Based on Regularization Method

提出一种基于正则化方法的高效图像复原技术.正则化残量的能量越小,则恢复效果越好,基于此,利用小波变换定性地分析如何选取正则化算子,利用随机理论得到正则化残量的能量期望值,通过最小化这个期望模型确定正则化参数,从而得到正则化图像.定性分析表明,在通常情况下应选取低阻高通的正则化算子.实验结果表明,该恢复技术比传统方法的恢复性能要好,恢复效果接近最佳且性能稳定.

A technique based on regularization method and restores image to close-to-optimal is proposed in this paper. The less the energy of the regularized residue, the better the image restoration. Based on this idea, wavelet transform is employed to choose regularization operator qualitatively, and stochastic theory is used to calculate the expectation of the energy, by minimizing the expectation to determine regularization parameter. Qualitative analysis concludes that the regularization operator should be low-stop and high-pass, and the experimental results show that the performances of this method are better than the traditional methods and yields steadily close-to-optimal restoration.