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
A fast compact algorithm for cubic spline smoothing
Computational Statistics & Data Analysis
Frequency-domain identification of continuous-time ARMA models from sampled data
Automatica (Journal of IFAC)
Efficient implementation of image interpolation as an inverse problem
Digital Signal Processing
B-LUT: Fast and low memory B-spline image interpolation
Computer Methods and Programs in Biomedicine
H∞ optimal approximation for causal spline interpolation
Signal Processing
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Fuzzy projection versus inverse fuzzy transform as sampling/interpolation schemes
Fuzzy Sets and Systems
Research on Interpolation Methods in Medical Image Processing
Journal of Medical Systems
Spline-based gradient filters for high-quality refraction computations in discrete datasets
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
Accelerated, high-quality refraction computations for volume graphics
VG'05 Proceedings of the Fourth Eurographics / IEEE VGTC conference on Volume Graphics
A new optimal seam finding method based on tensor analysis for automatic panorama construction
Pattern Recognition Letters
Computation of wavelet coefficients from average samples
Journal of Computational and Applied Mathematics
| Hi-index | 35.69 |
For pt.I see ibid., vol.41, no.2, p.821-33 (1993). A class of recursive filtering algorithms for the efficient implementation of B-spline interpolation and approximation techniques is described. In terms of simplicity of realization and reduction of computational complexity, these algorithms compare favorably with conventional matrix approaches. A filtering interpretation (low-pass filter followed by an exact polynomial spline interpolator) of smoothing spline and least-squares approximation methods is proposed. These techniques are applied to the design of digital filters for cubic spline signal processing. An efficient implementation of a smoothing spline edge detector is proposed. It is also shown how to construct a cubic spline image pyramid that minimizes the loss of information in passage from one resolution level to the next. In terms of common measures of fidelity, this data structure appears to be superior to the Gaussian/Laplacian pyramid