Geodesic
Image deblurring using a projective inertial parallel Subgradient extragradient-line algorithm of variational inequality problems
Filomat, Tome 36 (2022) no. 2, p. 423

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DOI

In this paper, we introduce projective inertial parallel subgradient extragradient-line algorithm for solving variational inequalites of L-Lipschitz continuous and monotone mappings which L is unknown. We prove a strong convergence result under some mild conditions in Hilbert space. We also present some numerical examples in Euclidean space R 3 compared with Parallel-Viscosity-Type Subgradient Extragradient-Line Method. Finally, we deblur the Grey and RGB images from common types of blur matrixes Gaussian blur, Out of focus blur and Motion blur using our proposed algorithm and show the better efficeincy when the number of types of blur matrixes is large
DOI : 10.2298/FIL2202423S
Classification : 40G05, 40D10, 40E05
Keywords: Variational inequality, Strong convergence, Hilbert space, Image deblurring, Subgradient extragradient-line algorithm 
S Suantai; P Peeyada; W Cholamjiak; H Dutta. Image deblurring using a projective inertial parallel Subgradient extragradient-line algorithm of variational inequality problems. Filomat, Tome 36 (2022) no. 2, p. 423 . doi: 10.2298/FIL2202423S
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     author = {S Suantai and P Peeyada and W Cholamjiak and H Dutta},
     title = {Image deblurring using a projective inertial parallel {Subgradient} extragradient-line algorithm of variational inequality problems},
     journal = {Filomat},
     pages = {423 },
     year = {2022},
     volume = {36},
     number = {2},
     doi = {10.2298/FIL2202423S},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.2298/FIL2202423S/}
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