000130313 001__ 130313 000130313 005__ 20240124152850.0 000130313 0247_ $$2doi$$a10.1109/TIP.2019.2924339 000130313 0248_ $$2sideral$$a114083 000130313 037__ $$aART-2019-114083 000130313 041__ $$aeng 000130313 100__ $$aSun, Tao 000130313 245__ $$aInertial Nonconvex Alternating Minimizations for the Image Deblurring 000130313 260__ $$c2019 000130313 5060_ $$aAccess copy available to the general public$$fUnrestricted 000130313 5203_ $$aIn image processing, total variation (TV) regularization models are commonly used to recover the blurred images. One of the most efficient and popular methods to solve the convex TV problem is the alternating direction method of multipliers (ADMM) algorithm, recently extended using the inertial proximal point method. Although all the classical studies focus on only a convex formulation, recent articles are paying increasing attention to the nonconvex methodology due to its good numerical performance and properties. In this paper, we propose to extend the classical formulation with a novel nonconvex alternating direction method of multipliers with the inertial technique (IADMM). Under certain assumptions on the parameters, we prove the convergence of the algorithm with the help of the Kurdyka-Lojasiewicz property. We also present numerical simulations on the classical TV image reconstruction problems to illustrate the efficiency of the new algorithm and its behavior compared with the well-established ADMM method. 000130313 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/E24-17R$$9info:eu-repo/grantAgreement/ES/MICINN/PGC2018-096026-B-I00$$9info:eu-repo/grantAgreement/ES/MICINN/MTM2015-64095-P 000130313 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000130313 590__ $$a9.34$$b2019 000130313 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b11 / 265 = 0.042$$c2019$$dQ1$$eT1 000130313 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b8 / 136 = 0.059$$c2019$$dQ1$$eT1 000130313 592__ $$a2.893$$b2019 000130313 593__ $$aSoftware$$c2019$$dQ1 000130313 593__ $$aComputer Graphics and Computer-Aided Design$$c2019$$dQ1 000130313 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000130313 700__ $$0(orcid)0000-0002-8089-343X$$aBarrio, Roberto$$uUniversidad de Zaragoza 000130313 700__ $$0(orcid)0000-0003-3426-105X$$aRodriguez, Marcos$$uUniversidad de Zaragoza 000130313 700__ $$aJiang, Hao 000130313 7102_ $$12005$$2595$$aUniversidad de Zaragoza$$bDpto. Matemática Aplicada$$cÁrea Matemática Aplicada 000130313 773__ $$g28, 12 (2019), 6211-6224$$pIEEE trans. image process.$$tIEEE TRANSACTIONS ON IMAGE PROCESSING$$x1057-7149 000130313 8564_ $$s3071385$$uhttps://zaguan.unizar.es/record/130313/files/texto_completo.pdf$$yPostprint 000130313 8564_ $$s3017666$$uhttps://zaguan.unizar.es/record/130313/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000130313 909CO $$ooai:zaguan.unizar.es:130313$$particulos$$pdriver 000130313 951__ $$a2024-01-24-15:02:19 000130313 980__ $$aARTICLE