An adaptive edge-preserving variational framework for color image regularization

In this paper, an adaptive edge-preserving variational framework for color image regularization is proposed. Unlike previous methods that emphasize only on different kinds of image regularization terms or use only constant image fidelity terms, in this framework, the balance between image regularization and fidelity terms is elegantly reached by adaptively assigning edge regions higher fidelity weights than homogenous regions. The adaptive fidelity weight is decided by a proposed edge indicator function together with the variational framework. The proposed framework achieves both good denoising and edge-preserving performance. Experimental results show the improved performance of our framework compared to other existing methods even in highly noised conditions.