基于期望值最大化的高光谱图像迭代复原算法

Iterative Restoration Algorithm Based on Expectation Maximization(EM)for Hyperspectral Image

声光可调谐滤光器(AOTF)的谱线半峰全宽(FWHM)以及换能器结构的不理想导致图像退化,空间分辨率降低。为了提高光谱数据的空间分辨率,将计算机断层图像复原中的期望值最大化(EM)算法应用到降质图像预处理中,可在对图像模糊降质程度估计不准确时进行运算,利用迭代求解逐次逼近最终收缩于原始目标。实验结果表明,该算法不依赖于数字图像周期拼接的假设,因而有效避免了传统的去卷积复原算法中产生的边界振铃现象,提高了图像的空间分辨率,图像质量得到改善。该算法对改善AOTF高光谱成像质量有较大意义。

The full width at half maximum （FWHM） of spectra of acousto-optic tunable filter （AOTF） and non-ideal transducer structure result directly in image degradation. In order to increase the spatial resolution of hyperspectral images, the expectation-maximization algorithm is ultilized and optimized in pre-processing, and it could also be used even when degration is not accurately estimated. The original target is ultinreately approached by iterative method. Experimental results show that the algorithm does not depend on the assumption of the image spliced periodically, and thus efficiently avoids the defects of the traditional deconvolution method. This method increases the spatial resolution of the image, and improves image quality. It plays a significant role in improving image quality of AOTF hyperspectral image.