Anisotropic diffusion filter based on spiking neural network model

Abstract

Image denoising is one of the most important steps in image processing. Anisotropic diffusion filters (ADFs), which are quite popular, stand out with their edge preservation properties, as well as their denoising success. However, ADFs determine edge pixels using the gradient value. In recent years, it has been observed that bioinspired studies based on the human visual system have yielded successful edge detection results. In this study, a new spiking neural network-based approach using the conductance-based integrate and fire neuron model is presented for the calculation of the ADF diffusion coefficient. The success of the proposed method was tested with 1100 noisy images derived from the BSDS300 Test dataset (100 original images) by adding additive white Gaussian noise. The SSIM and PSNR results showed that the proposed method is a very effective and efficient denoising filter.