Generalized Multiquadric Neural Networks in Image Reconstruction

Abstract

This work aims to numerically investigate the performance of the multiquadric (MQ) radial basis function in more general formats for image reconstruction applications. Desired features, i.e., accuracy and shape parameter sensitivity, of each form is numerically compared and explored. The famous Lena image is damaged using two levels of damage: 20% and 40%, in a Salt-and-Pepper manner. It has been discovered in this work that β=3/2 produces reasonably good accuracy and is least affected by the change in shape parameter while keeping both the CPU time and the condition number reasonably acceptable. This finding is promising and useful for further applications of MQ in more complex contexts.