Simultaneous Destriping and Denoising for Remote Sensing Images With Unidirectional Total Variation and Sparse Representation

被引:100
作者
Chang, Yi [1 ]
Yan, Luxin [1 ]
Fang, Houzhang [1 ]
Liu, Hai [1 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Automat, Key Lab, Minist Educ Image Proc & Intelligent Control, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
Denoising; destriping; remote sensing image; sparse representation; unidirectional total variation (UTV); NOISE-REDUCTION; MODIS DATA; ALGORITHMS;
D O I
10.1109/LGRS.2013.2285124
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Remote sensing images destriping and denoising are both classical problems, which have attracted major research efforts separately. This letter shows that the two problems can be successfully solved together within a unified variational framework. To do this, we proposed a joint destriping and denoising method by integrating the unidirectional total variation and sparse representation regularizations. Experimental results on simulated and real data in terms of qualitative and quantitative assessments show significant improvements over conventional methods.
引用
收藏
页码:1051 / 1055
页数:5
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