We consider in this paper the $l_0$-norm based dictionary learning approach combined with total variation regularization for the image restoration problem. It is formulated as a nonconvex nonsmooth optimization problem. Despite that this image restoration model has been proposed in many works, it remains important to ensure that the considered minimization method satisfies the global convergence property, which is the main objective of this work. Therefore, we employ the proximal alternating linearized minimization method whereby we demonstrate the global convergence of the generated sequence to a critical point. The results of several experiments demonstrate the performance of the proposed algorithm for image restoration.
Mohaoui, S., Hakim, A., & Raghay, S. (2021). A combined dictionary learning and TV model for image restoration with convergence analysis. Journal of Mathematical Modeling, 9(1), 13-30. doi: 10.22124/jmm.2020.15408.1369
MLA
Souad Mohaoui; Abdelilah Hakim; Said Raghay. "A combined dictionary learning and TV model for image restoration with convergence analysis". Journal of Mathematical Modeling, 9, 1, 2021, 13-30. doi: 10.22124/jmm.2020.15408.1369
HARVARD
Mohaoui, S., Hakim, A., Raghay, S. (2021). 'A combined dictionary learning and TV model for image restoration with convergence analysis', Journal of Mathematical Modeling, 9(1), pp. 13-30. doi: 10.22124/jmm.2020.15408.1369
VANCOUVER
Mohaoui, S., Hakim, A., Raghay, S. A combined dictionary learning and TV model for image restoration with convergence analysis. Journal of Mathematical Modeling, 2021; 9(1): 13-30. doi: 10.22124/jmm.2020.15408.1369
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