Paper Info
Title
Spline kernels for continuous-space image processing
Abstract
We present an explicit formula for spline kernels; these are defined as the convolution of several B-splines of variable widths h/sub i/ and degrees n/sub i/. The spline kernels are useful for continuous signal processing algorithms that involve B-spline inner-products or the convolution of several spline basis functions. We apply our results to the derivation of spline-based algorithms for two classes of problems. The first is the resizing of images with arbitrary scaling factors. The second is the computation of the Radon transform and of its inverse. In particular, we present a new spline-based version of the filtered back-projection algorithm for tomographic reconstruction. In both cases, our explicit kernel formula allows for the use of high-degree splines; these offer better approximation performance than the conventional lower-order formulations (e.g., piecewise constant or piece-wise linear models).
Year
DOI
Venue
2000
10.1109/ICASSP.2000.859272
ICASSP
Keywords
Field
DocType
spline kernel,high-degree spline,explicit formula,radon transform,resizing,new spline-based version,explicit kernel formula,approximation performance,filtered back-projection,b-spline inner-products,spline basis function,anti-aliasing,spline-based algorithm,continuous-space image processing,arbitrary scaling factor,kernel,biomedical imaging,tomography,spline,convolution,inner product,tomographic reconstruction,ofdm modulation,signal processing,image processing,large hadron collider,image reconstruction,filtered back projection
Spline (mathematics),Mathematical optimization,Thin plate spline,Box spline,Spline interpolation,Hermite spline,Convolution,Smoothing spline,Kernel (image processing),Mathematics
Conference
ISSN
ISBN
Citations 
1520-6149
0-7803-6293-4
4
PageRank 
References 
Authors
0.57
3
4
Name
Order
Citations
PageRank
S. Horbelt140.57
A. Munoz240.57
T Blu32574259.70
Unser, M.43438442.40