Perceptual Saliency Driven Total Variation for Image Denoising Using Tensor Voting

A nature image often contains various regions such as flat regions, ramps and edges with different singularities. A new perceptual saliency indicator is firstly proposed to distinguish edges and ramps. The proposed indicator is designed by a tensor voting approach with perceptual grouping performance. Using the perceptual saliency indicator, we propose a new variational model with an adaptive regularization term and a saliency weighted fidelity term. Experimental results demonstrate that our method has better performance in the staircase effect alleviation, the ramps and ridges preserving when compared with the state-of-the-art.