Image edge detection method based on anisotropic diffusion and total variation models

Abstract

In this study, a novel image edge detection technique based on the combination of total variation (TV) and anisotropic diffusion (PM) models is presented. In the proposed technique, the authors first use the gradient magnitude to eliminate the noise, then utilise the adaptive weight function to detect the edges of the image. The adaptive weight function has a high ability to adapt and change according to the areas information (edges or flats areas). More specifically, TV filter is applied on the areas which suffer from double and false edges, whereas, anisotropic diffusion filter is applied on the areas which suffer from weak and discontinuous edges. Applying TV filter on the double edges areas will allow one to remove most of the false edges, and thus to obtain much sharper edges. While, applying anisotropic diffusion filter on the discontinuous edges areas will lead to obtaining robust and continuous edges. Consequently, less false edges besides high localisation accuracy were obtained. Experimental results demonstrate the superiority of the new approach in terms of removing the false edges and improving the localisation accuracy of the edges. As objective quantitative performance measures, the peak signal-to-noise ratio (PSNR) and Pratt's figure of merit (FOM) were used.

Keywords

• edge detection
• image denoising
• image filtering
• discontinuous edges areas
• image edge detection method
• total variation models
• image edge detection technique
• adaptive weight function
• TV filter
• anisotropic diffusion filter
• double edges areas
• gradient magnitude
• noise elimination
• signal-to-noise ratio
• Pratt figure of merit