基于改进点扩散函数的遥感图像超分辨率重建

为了提高遥感图像空间域重建质量,采用改进凸集投影(POCS)算法的点扩散函数,提出了一种改进的POCS超分辨率重建算法。首先给出POCS算法基本原理以及具体实现步骤,在此基础上对算法做出改进,即对待重建的高分辨初始帧进行边缘检测,对检测到的边缘像素点应用改进的点扩散函数(PSF),使边缘处像素点对应的PSF水平方向与垂直方向系数依据边缘斜率变化而设置不同的权重;最后分别采用两组数据集对改进POCS算法的有效性进行验证。结果表明,该改进的POCS算法有效地提高了图像重建的效果,两组实验平均绝对误差效果分别提升了0.79%和0.26%,达到了提高图像重建质量的目的。该算法具有较好的实际应用价值。

Super-resolution reconstruction of remote sensing images based on the improved point spread function

In order to improve the quality of remote sensing image reconstruction in spatial domain, point spread function of improved projection onto convex set (POCS) algorithm was adopted and an improved POCS super-resolution reconstruction algorithm was proposed. Firstly, the basic principle and implementation steps of POCS algorithm were given. On this basis, the algorithm was improved and the reconstructed high-resolution initial frames were detected on edge. The improved point spread function (PSF) was applied to the detected edge pixels. The horizontal and vertical direction coefficients of PSF corresponding to the pixels at the edge were set with different weights according to the change of the slope of the edge. Finally, two sets of data sets were used to verify the effectiveness of the improved POCS algorithm. The results show that the improved POCS algorithm effectively improves the effect of image reconstruction. The average absolute errors of two groups increase by 0.79% and 0.26%, respectively. It achieves the goal of improving the quality of image reconstruction. The algorithm has good practical application value.