A new nonlocal total variation regularization algorithm for image denoising

摘要

The staircasing effect inevitably emerges in the recovered image via the local total variation (TV) based methods. To overcome this drawback, this paper elaborates on a novel nonlocal TV scheme associated with the quadratic perturbation of the ROF model for noise removal. Computationally, we present an improved split Bregman algorithm for minimizing the proposed energy functional recursively. Experimental results clearly demonstrate that our proposed strategy outperforms the corresponding TV scheme, especially in possessing higher computation speed and preserving the textures and fine details better when image denoising.