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Edge-preserving Image Smoothing via Counting-weighted Total Variation

We present a new counting-weighted total variation measure, which captures the consistency of gradient directions in local regions to distinguish edges and details. A novel optimization framework with the proposed counting-weighted total variation in the l1 regularization term is then developed to realize the edge-preserving image smoothing. In order to solve the optimization problem, we adopt an iteratively re-weighted least square based algorithm. Experimental results demonstrate that the proposed method is capable of completing edge-preserving image smoothing, while avoiding blurring and over-sharpening the edge. The proposed method can also be used for the image detail enhancement without involving halos or gradient reversal artifacts, while achieve better quality scores in the comparison with other enhancement methods.