Assigning Automatic Regularization Parameters in Image Restoration

This work aims to define and experimentally evaluate an adaptive strategy based on neural learning to select an appropriate regularization parameter to restore a degraded image. It is well known that selecting an appropriate regularization parameter is very difficult in regularized method. To solve this problem, we propose a novel method to construct the regularization parameter function through a training concept using a supervised neural network in an attempt to overcome the limitations of trial and error and curve fitting procedures. The proposed solution is not included within a particular restoration algorithm. The results of our experiments indicate that this method may yield a model that can be generalised to restore never seen images.