Super-resolution reconstruction of remote sensing image based on staggered pixels and non-uniform B-spline curved surface
WANG Jingmeng1,2,3,4, ZHANG Aiwu1,2,3,4, MENG Xiangang1,2,3,4, LIU Zhao1,2,3,4
1. College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China;
2. Laboratory of 3D Information Acquisition and Application, MOST, Beijing 100048, China;
3. Beijing Municipal Key Laboratory of Resources Environment and GIS, Beijing 100048, China;
4. State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Beijing 100048, China
The adoption of information from two images can improve resolution of images; nevertheless, the method based on interpolation is apt to make fussy boundary or cause partial loss of detailed information. To solve this problem, the authors employed an approach to super-resolution reconstruction based on staggered pixels and non-uniform B-spline surface. Two frames of images with low resolution,which were matched and geometrically registered at sub-pixel level,were adopted,and then these two images were staggered and re-sampled to double times of the origin grid;the positions with non-value were interpolated and filled with tri-B-spline surface interpolation,and non-uniform node parametric method was adopted. And the curved surface was composed of 36 known neighborhood pixels. For solving the value of interpolated points,the authors introduced parallel and golden section methods to iterate searching for the optimal value,which made interpolation more accurate; the last but not unimportant, the interpolated images were restored to reconstruct better visualization high resolution images. The assessment of the results demonstrates that this approach can considerably improve definition, quantity of information, signal-to-noise ratio and resolution.
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2015, Vol. 27 
