Continuous wavelet transform with arbitrary scales and O(N) complexity
Signal Processing
Edge-and-corner preserving regularization for image interpolation and reconstruction
Image and Vision Computing
Computer Aided Geometric Design
Image Magnification by a Compact Method with Preservation of Preferential Components
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Image thumbnails that represent blur and noise
IEEE Transactions on Image Processing
Spatially variant convolution with scaled B-splines
IEEE Transactions on Image Processing
Super-resolution with sparse mixing estimators
IEEE Transactions on Image Processing
A new algorithm to extract the lines and edges through orthogonal projections
Digital Signal Processing
Fast and robust filtering-based image magnification
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Color texture analysis using CFA chromatic co-occurrence matrices
Computer Vision and Image Understanding
A neural-AdaBoost based facial expression recognition system
Expert Systems with Applications: An International Journal
An SVM-AdaBoost facial expression recognition system
Applied Intelligence
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We present an optimal spline-based algorithm for the enlargement or reduction of digital images with arbitrary (noninteger) scaling factors. This projection-based approach can be realized thanks to a new finite difference method that allows the computation of inner products with analysis functions that are B-splines of any degree n. A noteworthy property of the algorithm is that the computational complexity per pixel does not depend on the scaling factor a. For a given choice of basis functions, the results of our method are consistently better than those of the standard interpolation procedure; the present scheme achieves a reduction of artifacts such as aliasing and blocking and a significant improvement of the signal-to-noise ratio. The method can be generalized to include other classes of piecewise polynomial functions, expressed as linear combinations of B-splines and their derivatives