Abstract
Linear prediction (LP) methods have been widely used for high-resolution spectral estimation from finite Fourier samples. Their application to image reconstruction, on the other hand, has been markedly less successful. In this article, we present an improved LP method for high-resolution image reconstruction. The distinguishing feature of the proposed method is its use of a generalized series model to enforce the data consistency constraint to compensate for reconstruction error resulting from LP modeling. Several reconstruction examples from magnetic resonance imaging data are included to demonstrate the performance of the method.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 136-140 |
| Number of pages | 5 |
| Journal | International Journal of Imaging Systems and Technology |
| Volume | 7 |
| Issue number | 2 |
| DOIs | |
| State | Published - 1996 |
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Software
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering