Diffusion neural network model for image-preprocessing

In this letter we propose a neural network model that performs the Gaussian operation efficiently by the diffusion process. Diffusion of an external spot excitation to the neighbouring pixels results in a Gaussian distribution. We apply this diffusion model to the DOG (difference of Gaussian) operation to detect the intensity changes in an image. In this model each cell has four fixed-weighted interconnections to the neighboring cells for a two-dimensional image. A different spatial frequency component can be obtained in each step of a sequential diffusion process. Therefore, the diffusion model is simpler and more efficient than the well-known LOG masking method. As far as we know, this is the only model for edge detection that can be implemented in hardware.<
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