增量维纳滤波法在波前探测解卷积中的应用

一个常规的自适应光学系统通常包含三个重要环节：波前探测、波前校正和波前重构。因此对系统的技术要求非常高，造成系统复杂，成本昂贵。基于哈特曼-夏克波前探测的图像解卷积处理就是“事后”处理的一种，它省去了波前校正环节，使常规的自适应光学系统得到简化，降低了系统成本。其基本原理为对瞬时波面进行短时间曝光探测，同时记录相应的短时间曝光图像，来进行解卷积处理。将增量维纳滤波法应用于基于哈特曼-夏克波前探测的解卷积中，并对室内模拟点源情况下的三组畸变光斑图像数据进行了解卷积恢复处理。结果表明，将增量维纳滤波法应用于基于波前探测的解卷积是完全可行的，在室内模拟点源情况下，恢复的图像可以达到衍射极限分辨力。与维纳滤波相比，它扩展了噪声抑制因子的选取范围，在噪声抑制因子选取不正确的情况下，仍能得到比维纳滤波更好的结果。

The Application of Increamental Wiener Filters in Image Deconvolution of Wavefront Sensing

An adaptive-optics system usually contains wavefront sensing, wavefront conrection, wavefront reconstructing. So the system is complex and expensive. Deconvolution based on Hartmann-Shark wavefront sensing is a kind of “post-processing”, for it does not need wavefront correction and the system is less complex and less expensive. The principle is sensing wavefront in short exposal and recording blurred images at the same time, which are used in deconvolution. Incremental Wiener filter is first applied to image deconvolution from wavefront sensing. Images of point sources indoors with aberration are processed and restored by incremental Wiener filters. The results show that incremental Wiener filters can efficiently be used in image deconvolution from wavefront sensing. The resolution of restored images can reach a diffraction limit in terms of point sources indoors. Compared with Wiener filter, it extends the selection range of noise restrain coefficient. Even the noise restrain coefficient is not properly chosen, it still has better restored image quality than Wiener filters.