@inproceedings{4de06c3b51904c758302e63ee1b8d852,
title = "Edge-adaptive structure tensor nonlocal kernel regression for removing cloud",
abstract = "This paper the authors applies structure tensor matrix as a tool in order to survey the anisotropic structure of remote sensing image and combines nonlocal kernel regression methods for removing cloud tasks. The method utilizes both local structural regularity and the nonlocal self-similarity properties in remote sensing images. The nonlocal self-similarity takes advantages of observation that image patches incline to repeat themselves in remote sensing images. The non-local prior avails of the redundancy of similar patches remote sensing images, while the local prior consider that a target pixel can be computed by a weighted average of its neighbors. Experimental results indicate that our new algorithm better than both the steering kernel regression in persevering edge and improve the visual quality.",
keywords = "Cloud, Kernel regression, Nonlocal Self-similarity, Structure tensor",
author = "Guohong Liang and Ying Li and Junqing Feng",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; International Conference on Intelligent and Interactive Systems and Applications, IISA2016 ; Conference date: 25-06-2016 Through 26-06-2016",
year = "2017",
doi = "10.1007/978-3-319-49568-2\_55",
language = "英语",
isbn = "9783319495675",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "383--389",
editor = "Fatos Xhafa and Srikanta Patnaik and Zhengtao Yu",
booktitle = "Recent Developments in Intelligent Systems and Interactive Applications - Proceedings of the International Conference on Intelligent and Interactive Systems and Applications, IISA 2016",
}