Modified total variation regularization using fuzzy complement for image denoising

In this paper, we propose a denoising algorithm based on the Total Variation (TV) model. Specifically, we associate to the regularization term of the Rodin-Osher-Fatimi (ROF) functional a small weight whenever denoising is performed in edge and texture regions, which means less regularization and more details preservation. On the other hand, a large weight is associated if the region being filtered is smooth which means noise will be well suppressed. The weight computation is inspired from the fuzzy edge complement. Experiments on well-known images and comparison with state of the art denoising algorithms demonstrate that the proposed method not only presents good denoising performance but also is able to preserve edge information.