Polynomial preserving algorithm for digital image interpolation
Signal Processing
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
An axiomatic approach to image interpolation
IEEE Transactions on Image Processing
Regularity-preserving image interpolation
IEEE Transactions on Image Processing
Lapped nonlinear interpolative vector quantization and image super-resolution
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
Predictive classified vector quantization
IEEE Transactions on Image Processing
Subpixel edge localization and the interpolation of still images
IEEE Transactions on Image Processing
Locally edge-adapted distance for image interpolation based on genetic fuzzy system
Expert Systems with Applications: An International Journal
Two-stage interpolation algorithm based on fuzzy logics and edges features for image zooming
EURASIP Journal on Advances in Signal Processing
Research on Interpolation Methods in Medical Image Processing
Journal of Medical Systems
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According to advances in digital imaging technology, interest in high-resolution (HR) images has been increased. Various methods that convert low-resolution (LR) images to HR ones have been presented. In this paper, to reduce the computational load we propose a vector quantization (VQ) based algorithm that reconstructs an interpolation image by adding to an initially interpolated image high-frequency components predicted from training with a number of example image sets. The proposed interpolative classified VQ (ICVQ) algorithm combines interpolative VQ with classified VQ. With a number of (LR and HR) example image sets, we construct two types of (LR and HR) codebooks. Comparative experiments with three conventional image interpolation algorithms show that the proposed interpolation algorithms using ICVQ effectively preserve edges to which the human visual system is sensitive. The proposed algorithm can be applicable to various image- and video-based applications such as digital camera and digital television.