Robust Super-Resolution Image Reconstruction Method for Geometrically Deformed Remote Sensing Images
Robust Super-Resolution Image Reconstruction Method for Geometrically Deformed Remote Sensing Images
复制标题
几何变形遥感图像鲁棒超分辨率图像重建方法
DOI：
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发表时间：
2018
期刊：
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通讯作者：
I. Yanovsky
中科院分区：
文献类型：
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作者：
Jing Qin;I. Yanovsky
由于成像传感器的局限性，遥感图像的分辨率往往有限。为了解决这个问题，各种超分辨率(SR)图像重建技术已经被开发出来，以从低分辨率、噪声和模糊的观测序列中重建高分辨率图像。提出了一种基于非局部全变差(NLTV)正则化的几何形变遥感图像超分辨率重建方法。提出的极小化问题用一种快速的原始-对偶算法来求解。数值实验证明了该方法的有效性。
Due to the limitations of imaging sensors, remote sensing images often have limited resolution. To address this issue, various super-resolution (SR) image reconstruction techniques have been developed to reconstruct a high-resolution image from a sequence of low-resolution, noisy and blurry observations. In this paper, we propose an efficient super-resolution image reconstruction method for geometrically deformed remote sensing images, based on the nonlocal total variation (NLTV) regularization. The proposed minimization problem is solved by a fast primal-dual algorithm. Numerical experiments demonstrate the performance of the proposed method.