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A CNN-based computational algorithm for nonlinear image diffusion problem
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DOI：10.1007/s11042-020-09077-0.png)
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Abstract
En 中文
In the past, several partial differential equations (PDEs) based methods have been widely studied in image denoising. While solving these methods numerically, some parameters need to be chosen manually. This paper proposes a cellular neural network (CNN) based computational scheme for solving the nonlinear diffusion equation modeled for removing additive noise of digital images. The diffusion acts like smoothing on the noisy image, which is taken as an initial condition for the nonlinear PDE. In the proposed scheme, the template matrices of CNN evolve during the iterative diffusion and act as edge-preserving filters on the noisy images. The evolving diffusion ensures convergence of the diffusion process after a specific diffusion time. Therefore, the advantages of such a CNN-based solution scheme are more accurate restoration in terms of image quality with low computation and memory requirements. The experimental results show the effectiveness of the proposed algorithm on different sets of benchmark images degraded with additive noise.
Keywords:
Additive Gaussian noise
Cellular neural network
Edge preserving filters
Image denoising
Nonlinear diffusion
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