This work contains the results of the experiments on the restoration of the defective images proceeded in a matrix and a vector form with the help of the feed forward neural network. Sometimes it is convenient to represent an image as a vector rather than as a matrix. So the target of this work is to show experimentally what kind of input provides a better restoration, judging from the Euclid's distance of the output of a trained network. This work also shows the differences between processing different types of image presentation of the neuron network. Making a comparative analysis of a matrix and a vector form of presenting the images which are proceeded to a feed forward network allows stating some specific characteristics of a network. These characteristics include the optimal architecture of a network, the number of layers, the number of neurons in each layer and the time of an image restoration. Taking into account the network's characteristics and the most important factor - the Euclid's distance, are drawn conclusions that concern what is the best way of representing images that we want to restore using a feed forward network