基于非局部相似性和低秩矩阵的遥感图像重构方法

Remote Sensing Image Reconstruction Method Based on Non-Local Similarity and Low Rank Matrix

针对遥感图像非局部相似的特性,提出了一种基于图像非局部相似性、低秩矩阵和最小全变分(TV)的压缩感知(CS)重构算法。充分利用了遥感图像的非局部相似性先验、局部平滑性先验以及低秩矩阵的特性,同时引入了一种新的基于欧氏距离和结构相似度的联合块匹配方式,使匹配结果更准确,最终实现了高质量的遥感图像重构。仿真结果表明,与传统的基于变换域稀疏或TV约束的重构算法相比,所提出的算法能获得更高的图像重构质量,峰值信噪比和结构相似度等评价值都有较大的提高,验证了算法的有效性。

A compressed sensing reconstruction method based on nonlocal similarity,low rank matrix and minimum total variation（TV）is proposed,considering the non-local similarity of remote sensing images.It fully exploits the nonlocal similarity prior,local smoothness prior of remote sensing images and the low rank properties of matrix.A new joint block matching method based on Euclidean distance and structural similarity is developed,which makes the matching result more accurate.The reconstruction of high quality remote sensing image is realized finally.Simulation results confirm that the proposed algorithm can achieve high reconstruction quality comparing with the traditional reconstruction method based on sparse transform domain or TV regularization.The peak signal to noise ratio and structural similarity have a great improvement,and the effectiveness of the proposed method is verified.