Impulse Noise Image Restoration Using Nonconvex Variational Model and Difference of Convex Functions Algorithm

• Published in: IEEE transactions on cybernetics Vol. 54; no. 4; pp. 2257 - 2270
• Main Authors: Zhang, Benxin, Zhu, Guopu, Zhu, Zhibin, Zhang, Hongli, Zhou, Yicong, Kwong, Sam
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Convex analysis; Convex functions; Convexity; Data models; Difference of convex functions algorithm (DCA); Image edge detection; Image restoration; impulse noise; Mathematical models; Noise measurement; nonconvex optimization model; Regularization; Staircases
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2022.3225525

In this article, the problem of impulse noise image restoration is investigated. A typical way to eliminate impulse noise is to use an <inline-formula> <tex-math notation="LaTeX">L_{1} </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">L_{1} </tex-math></inline-formula> 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.