IEEE International Conference on System, Man and Cybernetics, vol. 2, pp. 1671-1675, Oct. 1995


 An Adaptive Filtering Interpolator Using Neural Networks 

Zhou Wang, and Yinglin Yu

Department of Communication & Electronic Engineering, South China Univ. of Technology, Guangzhou, China

 PDF File (416K)

Abstract: 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 alpha should be predetermined. The authors think if the parameter 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 alpha and the best values of alpha for certain images are much different. Therefore, how to determine the values of 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 alpha . A modified robust BP algorithm is used in the training procedure for the authors' special use. Simulation results show that 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 alpha , interpolation results are also improved.