Efficient algorithms for hybrid regularizers based image denoising and deblurring

To better eliminate the staircase effect and simultaneously preserve edge details, this paper investigates a hybrid regularizers model for image denoising and deblurring. This technique closely incorporates the advantages of the classical total variation (TV) filter and the fourth-order filter. Computationally, we develop an extremely efficient relaxation scheme and alternating minimization algorithm, and give the rigorous convergence analyses there in detail. Provided experimental results distinctly illustrate the high efficiency of the addressed numerical algorithms. Also, in comparison with the recovered results by the state-of-the-art models, simulations manifestly demonstrate the competitive performance of our proposed scheme in reducing the blocky images and sharply maintaining the edge features.