An adaptive filtering interpolator using neural networks
Zhou Wang, Yinglin Yu · 2002
Filtering interpolators presented by Lucke and Stocker (1993) have advantages in reducing interpolation error in image background clutter-suppression systems especially for data with low sampling rates. Before they are to be applied, a fixed parameter /spl alpha/ should be predetermined. The authors think if the parameter /spl alpha/ is well adjusted, it may also be useful to recover an image from a less densely sampled image. Experiments show that interpolation error relies greatly on the parameter /spl alpha/ and the best values of /spl alpha/ for certain images are much different. Therefore, how to determine the values of /spl alpha/ becomes the key problem for this application. In this paper, the authors develop a neural network based adaptive system to automatically adjust the value of /spl alpha/. A modified robust BP algorithm is used in the training procedure for the authors' special use. Simulation results show that /spl alpha/ can be generated automatically by the neural networks instead of being blindly predetermined to a fixed value. Compared to the interpolator with best fixed parameter /spl alpha/, interpolation results are also improved.