Remote Sensing Image Inpainting Based on Non-local Sample Filling and Adaptive Curvature Driven Diffusions Model
WANG Xianghai1,2, SUN Li1, WAN Yu2, WANG Shuang1, TAO Jingzhe3
1.School of Computer and Information Technology, Liaoning Normal University, Dalian 116029 2.School of Mathematics, Liaoning Normal University, Dalian 116029 3.College of Urban and Environmental Science, Liaoning Normal University, Dalian 116029
Abstract Remote sensing image inpainting technology is significant for the following treatment and application of remote sensing image. Based on a thorough study of curvature driven diffusions (CDD) model and sample filling algorithm, a remote sensing image inpainting algorithm based on non-local sample filling and adaptive curvature driven diffusion model is proposed. The proposed algorithm can avoid the false edge, the staircase effect, the slow diffusion velocity, etc. in some extreme cases during the process of image inpainting. Meanwhile, it maintains the texture feature and edge information well for the inpainted image. The proposed algorithm is verified by simulation experiments.
Fund:Supported by National Natural Science Foundation of China (No.61402214,41271422), Research Fund for the Doctoral Program of Higher Education (No.20132136110002), Doctoral Scientific Research Foundation of Liaoning Province (No.20121076), Scientific Research General Project of Department of Education of Liaoning Province (No.L2014423)
About author:: (WANG Xianghai, born in 1965, Ph.D., professor. His research interests include computer graphics and multimedia information processing.)(SUN Li, born in 1990, master student. Her research interests include image inpainting based on partial differential equation.)(WAN Yu, born in 1990, master student. Her research inte-rests include image processing based on partial differential equation.)(WANG Shuang, born in 1987, master student. Her research interests include image processing based on partial differential equation.)(TAO Jingzhe(Corresponding author), born in 1985, Ph.D. candidate. His research interests include remote sensing image information processing.)
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2016, Vol. 29 
