Abstract
AbstractPositive definite kernels are powerful tools for multivariate approximation from scattered data. This contribution discusses kernel‐based image approximation from scattered Radon data. To this end, we use weighted kernels for the reconstruction. Moreover, we propose greedy algorithms, which are used to adaptively select suitable approximation spaces. This reduces the complexity of the resulting image reconstruction method and, moreover, it improves the numerical stability quite significantly.
Full Text 
Sign-in/Register to access full text options
Sign-in/Register to access full text options 
Published version (
Free)
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
