A new method of image denoising based on cellular neural networks

Abstract

This paper presents an edge constraint adaptive filtering algorithm based on cellular neural networks for image denoising. In the process of designing the three templates separately in cellular neural networks, the control template references the advantage of spatial filtering denoising. It resembles a spatial domain denoising filter. The feedback template sets as a matrix which generated by a high pass filter to achieve edge preservation. The proposed method can not only achieve denoising, but also protect edges in an image. In the process of designing the threshold template, we use the different gray levels in an image to achieve the threshold adjustment adaptively. The experiment simulation results show that this algorithm is effective. Its denoising effect is much better than the mean filtering, median filtering, Gaussian filtering and the non local means method. And compared with the anisotropic diffusion algorithm, this algorithm is also better for the impulsive noise (salt & pepper noise), the Poisson noise and the comprehensive noise denoising. Due to the parallelism and possible hardware implementation of cellular neural network, it can achieve real time image denoising, which has a good application prospect.

Keywords

• cellular neural networks
• image denoising
• spatial filtering
• adaptive edge constraint