ENAS-RIF图像复原算法

ENAS-RIF algorithm for image restoration

大气湍流严重影响天文、遥感等光学观测的成像效果,必须进行图像复原处理后才能获取更清晰的图像。为了提高图像复原效果,提出了一种基于可靠支持域和改进代价函数的增强型非负性和有限支撑域的递归逆滤波器(ENAS-RIF)图像复原算法。首先,利用Curvelet去噪进行图像平滑的预处理,抑制图像噪声;然后利用图像阈值分割和形态学膨胀操作获取目标的可靠支持域,以加快算法的的收敛速度;再根据支持域提取结果将不均匀背景全部设置为背景均值,解决背景不均匀的局限;最后在代价函数中增加目标边缘的保持约束项,并进一步引入单调平滑的对数代价函数改进算法的收敛性和稳定性。实验结果表明,改进后的ENAS-RIF算法具有更好的复原效果和更快的收敛速度。

Imaging of objects is inevitably encountered by space-based,ground-based working in the atmospheric turbulence environment,such as those used in astronomy,remote sensing and so on.The observed images are seriously blurred.The restoration is required for reconstruction turbulence degraded images.In order to enhance the performance of image restoration,a novel enhanced nonnegativity and support constrants recursive inverse filtering（ENAS-RIF） algorithm was presented,which was based on the reliable support region and enhanced cost function.Firstly,the Curvelet denoising algorithm was used to weaken image noise.Secondly,the reliable object support region estimation was used to accelerate the algorithm convergence.Then,the average gray was set as the gray of image background pixel.Finally,an object construction limit and the logarithm function were add to enhance algorithm stability.The experimental results prove that the convergence speed of the novel ENAS-RIF algorithm is faster than that of NAS-RIF algorithm and it is better in image restoration.