An l(0)-overlapping group sparse total variation for impulse noise image restoration

• Main Authors: Yin, Mingming, Adam, Tarmizi, Paramesran, Raveendran, Hassan, Mohd Fikree
• Format: Article
• Published: Elsevier 2022
• Subjects: TA Engineering (General). Civil engineering (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.um.edu.my/43076/

Total variation (TV) based methods are effective models in image restoration. For eliminating impulse noise, an effective way is to use the l(1)-norm total variation model. However, the TV image restoration always yields staircase artifacts, especially in high-density noise levels. Additionally, the l(1)-norm tends to over penalize solutions and is not robust to outlier characteristics of impulse noise. In this paper, we propose a new total variation model to effectively remove the staircase effects and eliminate impulse noise. The proposed model uses the l(0)-norm data fidelity to effectively remove the impulse noise while the overlapping group sparse total variation (OGSTV) acts as a regularizer to eliminate the staircase artifacts. Since the proposed method requires solving an l(0)-norm and an OGSTV optimization problem, a formulation using the mathematical program with equilibrium constraints (MPEC) and the majorization-minimization (MM) method are respectively used together with the alternating direction method of multipliers (ADMM). Experiments demonstrate that our proposed model performs better than several state-of-the-art algorithms such as the e1 total generalized variation, e0 total variation, and the l(1) overlapping group sparse total variation in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM).