Sparse Representation of Images based on RBF Neural Network

In this paper, we present a model and corresponding algorithm for sparse representing of image data. We use a RBF neural network with a small number of ellipse Gaussian functions to approximate the input images. The sparisty of the image data is achieved by solving a nonlinear optimization problem with
$L_{1}$
-regularization in our model. Experimental results illustrate that our method can represent the input images with good accuracy by much less number of ellipse Gaussian functions. The method is applicable for representing general surfaces and shapes.