基于最佳倒易晶胞的斜采样遥感图像复原方法

Tilting Mode Sampling Image Restoration Based on Optimal Reciprocal Cell

遥感图像的退化受多种因素的影响。本文研究了斜采样方式下混叠、模糊、噪声对图像有效分辨率的影响,在贝叶斯框架下提出了一种基于最佳倒易晶胞的斜采样遥感图像复原方法。该方法基于高斯图像先验假设,首先根据斜采样图像获取系统参数对倒易晶胞的形状进行限制,获取混叠与噪声最小的频域覆盖,然后在Bayesian-MAP(最大后验概率,Maximum a Posterior Probability)框架下建立耦合模型,通过迭代复原方法得到混叠、模糊、噪声最小化的图像。实验结果说明,本算法考虑了引起图像混叠的图像成像模型特性,获得了较好的复原结果,增强了图像分辨率。

Remote sensing image is degraded by a variety of factors. In this paper, the impact of aliasing and noise on the tilting mode sampling remote sensing image is studied. A method for tilting mode sampling image restoration in the Bayesian frame is proposed to get optimal effective resolution. The Gaussian image prior assumption and the image acquisitive system parameters are used to get the optimal effective resolution based on reciprocal cell. The restoration in the Bayesian-MAP frame with the optimal reciprocal cell completes spectral extrapolation. Experimental results indicate that in the proposed method, the modeling of image acquisition systems is considered and an effective image restoration result is obtained, thus improving the image effective resolution.