FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Smart Interpolation by Anisotropic Diffusion
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
A Conditional Random Field Model for Video Super-resolution
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Consistent Sampling and Signal Recovery
IEEE Transactions on Signal Processing
High-quality image resizing using oblique projection operators
IEEE Transactions on Image Processing
Least-squares image resizing using finite differences
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Super-resolution of images based on local correlations
IEEE Transactions on Neural Networks
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Bicubic interpolation is one of the standard approaches for image magnification since it can be easily computed and does not require a priori knowledge nor a complicated model. In spite of such convenience, the images enlarged by bicubic interpolation are blurry, in particular for large magnification factors. This may be explained by four constraints of bicubic interpolation. Hence, by relaxing or replacing the constraints, we propose a new magnification method, which performs better than bicubic interpolation, but retains its compactness. One of the constraints is about criterion, which we replace by a criterion requiring that all pixel values are reproduced and preferential components in input images are perfectly reconstructed. We show that, by choosing the low frequency components or edge enhancement components in the DCT basis as the preferential components, the proposed method performs better than bicubic interpolation, with the same, or even less amount of computation.