An ℓ0-overlapping group sparse total variation for impulse noise image restoration

• Published in: Signal processing. Image communication 2022-03, Vol.102, p.1, Article 116620
• Main Authors: Yin, Mingming, Adam, Tarmizi, Paramesran, Raveendran, Hassan, Mohd Fikree
• Format: Article
• Language: English
• Subjects: [formula omitted]-norm fidelity; ADMM; Algorithms; Image restoration; Noise; Noise levels; Non-convex; Optimization; Outliers (statistics); Robustness (mathematics); Signal to noise ratio; Total variation
• ISSN: 0923-5965; 1879-2677
• DOI: 10.1016/j.image.2021.116620

Total variation (TV) based methods are effective models in image restoration. For eliminating impulse noise, an effective way is to use the ℓ1-norm total variation model. However, the TV image restoration always yields staircase artifacts, especially in high-density noise levels. Additionally, the ℓ1-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 ℓ0-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 ℓ0-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 ℓ1 total generalized variation, ℓ0 total variation, and the ℓ1 overlapping group sparse total variation in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). •The proposed model robustly eliminates impulse noise and the staircase artifacts.•The proposed model efficiently solves the non-convex model.•Significantly better results for both image denoising and deblurring.