An adaptive cubic convolution image interpolation approach
Machine Graphics & Vision International Journal
Edge-and-corner preserving regularization for image interpolation and reconstruction
Image and Vision Computing
Optimization Methods & Software - GLOBAL OPTIMIZATION
Interactively controlling the smoothing and postaliasing effects in volume visualization
Proceedings of the 25th Spring Conference on Computer Graphics
Edge-directed image interpolation using color gradient information
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Fast and robust filtering-based image magnification
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Local geometry driven image magnification and applications to super-resolution
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Image magnification method based on linear interpolation and wavelet and PDE
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Vehicle license plate super-resolution using soft learning prior
Multimedia Tools and Applications
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We present a simple, original method to improve piecewise-linear interpolation with uniform knots: we shift the sampling knots by a fixed amount, while enforcing the interpolation property. We determine the theoretical optimal shift that maximizes the quality of our shifted linear interpolation. Surprisingly enough, this optimal value is nonzero and close to 1/5. We confirm our theoretical findings by performing several experiments: a cumulative rotation experiment and a zoom experiment. Both show a significant increase of the quality of the shifted method with respect to the standard one. We also observe that, in these results, we get a quality that is similar to that of the computationally more costly "high-quality" cubic convolution.