基于改进维纳逆滤波的衍射成像光谱仪数据误差分析与重构

衍射成像光谱仪探测到的高光谱数据需要进行计算与反演才可以得到成像光谱数据,本文对衍射成像光谱仪的成像过程及数据误差产生的原理从空间维和光谱维两方面进行了分析,并针对其光谱重构过程中系统点扩散函数标准差较大时重构结果清晰度较低、存在振铃等问题,提出了基于改进维纳逆滤波的光谱数据重构算法,该方法在分析衍射成像光谱仪数据特点与误差的基础上,将每一次维纳逆滤波的重构结果视为新的模糊图像,利用成像过程及维纳逆滤波的基本原理确定新的模糊图像对应的点扩散函数,反复进行维纳逆滤波达到提高图像清晰度的效果,再根据图像自身的空间和光谱特征分布,进行自适应性的噪声去除.利用模拟的衍射成像光谱数据进行验证,在系统点扩散函数的标准差为2.5的情况下,能得到无振铃的重构结果,且与传统维纳逆滤波法的重构结果进行比较,清晰度、细节能力等指标均有所提高,满足了衍射成像光谱数据应用需求.

Error analysis and reconstruction for diffractive optic imaging sp ectrometer using the multiple iterations

Diffraction imaging spectrometer cannot acquire imaging spectral data without calculation and inversion. In this paper, the imaging process of the diffraction imaging spectrometer and the principle of the data error from both space and spectra are analyzed. To solve the problems of low definition of the reconstruction and the ringing in it occurring under the condition that the point spread function (PSF) is larger, a new algorithm is proposed based on improved inverse Wiener filtering. The improved method regards the reconstruction result of Wiener filtering as a new fuzzy image, and recalculates the PSF of the new fuzzy image based on the analysis of the diffraction characteristics and error. Inverse iterative Wiener filtering is used to improve the definition of the reconstruction, and then the noise needs to be removed according to the distribution of the spatial and spectral features. Simulated diffraction imaging spectral data are used to verify the correctness of the algorithm proposed in this paper. A reconstruction without ringing can be obtained when the standard deviation of PSF is 2.5, and both of the definition and detail ability are higher than those of the traditional reconstruction. The reconstruction using the improved algorithm proposed in this paper can satisfy the applications of the diffraction imaging spectral data.
Keywords： diffractive optic image hyperspectral data spectral reconstruction point spread function