基于记忆效应的散斑解卷积法是近几年提出的一种可以实现透过散射介质层成像的方法。可用于散斑解卷积法的算法有很多,但具体的对比分析工作却鲜有报道。设计并搭建了基于记忆效应的透过散射层成像的光学系统,对探测到的散斑进行解卷积计算,并重建出对象图像。在重建过程中,分别使用互相关解卷积算法、维纳滤波算法、正则化解卷积算法以及Lucy-Richardson算法进行解卷积计算。对不同算法重建的图像进行了多个图像质量评价指标的计算。综合图像质量和计算时间,发现互相关解卷积算法在透过散射层成像的应用中具有最大优势,并从原理上进行了简要的解释。

The memory-effect based speckle deconvolution technique is a method recently proposed to realize imaging through scattering layers. There exist many algorithms used in speckle deconvolution, but there seldom exists specific comparative analysis work so far. A system for memory-effect based imaging through scattering layers is first designed and built. Then the detected speckles are deconvoluted to reconstruct the object image. During the progress of reconstruction, the cross-correlation deconvolution algorithm, the Wiener filtering algorithm, the regularized deconvolution algorithm and the Lucy-Richardson algorithm are respectively used for deconvolution. A number of image quality evaluation indexes are calculated for the images recovered by different algorithms. Considering the image quality and the computation time, it is concluded that the cross-correlation deconvolution algorithm has the greatest advantage in the application of imaging through scattering layers, which is briefly explained in principle.