Texture synthesis by a neural network model

In this paper we propose a neural network model to synthesise texture images. The model is based on a continuous Hopfield-like network where each pixel of the image is occupied by a neuron that is eight-connected to its neighbours. A state of the neuron denotes a certain grey level of the corresponding pixel. The firing of the neuron changes its state, and hence the grey level of the corresponding pixel. Different two-tone and grey-tone texture images can be synthesised by manipulating the connection weights and by varying the algorithm iteration number. For grey-tone texture synthesis, a Markov chain principle has been employed to decide on the multiple state transition of a neuron. The model can be employed for texture propagation with the advantage that it allows propagation without showing any blocky effect.