Impulse Noise Image Restoration Using Nonconvex Variational Model and Difference of Convex Functions Algorithm - Zhang, Benxin; Zhu, Guopu; Zhu, Zhibin; Zhang, Hongli; Zhou, Yicong; Kwong, Sam (IEEE transactions on cybernetics, 2024)
In this article, the problem of impulse noise image restoration is investigated. A typical way to eliminate impulse noise is to use an L_{1} norm data fitting term and a total variation (TV) regularization. However, a convex optimization method designed in this way always yields staircase artifacts. In addition, the L_{1} norm fitting term tends to penalize corrupted and noise-free data equally, and is not robust to impulse noise. In order to seek a solution of high recovery quality, we propose a new variational model that integrates the nonconvex data fitting term and the nonconvex TV regularization. The usage of the nonconvex TV regularizer helps to eliminate the staircase artifacts. Moreover, the nonconvex fidelity term can detect impulse noise effectively in the way that it is enforced when the observed data is slightly corrupted, while is less enforced for the severely corrupted pixels. A novel difference of convex functions algorithm is also developed to solve the variational model. Using the variational method, we prove that the sequence generated by the proposed algorithm converges to a stationary point of the nonconvex objective function. Experimental results show that our proposed algorithm is efficient and compares favorably with state-of-the-art methods.