A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Curve intersection using Be´zier clipping
Computer-Aided Design - Special Issue: Be´zier Techniques
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Perceptually-Inspired and Edge-Directed Color Image Super-Resolution
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH Asia 2008 papers
Coordinates for instant image cloning
ACM SIGGRAPH 2009 papers
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As the resolution of output device increases, the demand of high resolution contents has become more eagerly. Therefore, the image superresolution algorithms become more important. In digital image, the edges in the image are related to human perception heavily. Because of this, most recent research topics tend to enhance the image edges to achieve better visual quality. In this paper, we propose an edge-preserving image super-resolution algorithm by vectorizing the image edges. We first parameterize the image edges to fit the edges' shapes, and then use these data as the constraint for image superresolution. However, the color nearby the image edges is usually a combination of two different regions. The matting technique is utilized to solve this problem. Finally, we do the image super-resolution based on the edge shape, position, and nearby color information to compute a digital image with sharp edges.