抽象的

Optimization of Gradient Threshold Parameter in Feature Preserving Anisotropic Diffusion for Image Denoising

Reena Singh , V.K.Srivastava

Image denoising emphasizes on noise removal while preserving meaningful details such as blurred thin edges and low contrast fine features. In this work, feature preservation anisotropic diffusion is proposed which not only removes noise but also has the capability of preserving fine details even of low contrast in the denoised image. This type of filtering technique is also highly dependent on some crucial parameters of filtering such as conductance function, gradient threshold parameter and stopping time. This paper also focuses on the optimization of gradient threshold parameter. The alternative options for the parameters of anisotropic diffusion at each stage of the algorithm are examined, evaluated and the best choice is selected. Experimental results evaluated on standard test images have shown that the proposed anisotropic diffusion gives better results in terms of subjective and objective measure in respect to other compared diffusion techniques.