An adaptive image inpainting method based on the modified mumford-shah model and multiscale parameter estimation
Thanh D.N.H., Prasath V.B.S., Son N.V., Hieu L.M.

 

Department of Information Technology, Hue College of Industry, Hue 530000 VN,
Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229 USA,
Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH 45267 USA
Department of Electrical Engineering and Computer Science, University of Cincinnati, OH 45221 USA,
Department of Robotics and Production Adaptation, Tula State University, Tula 300012, Russia,
Ballistic Research Laboratory, Military Weapon Institute, Hanoi 100000, Vietnam,
Department of Economics, University of Economics, The University of Danang, Danang 550000, Vietnam

Abstract:
Image inpainting is a process of filling missing and damaged parts of image. By using the Mumford-Shah image model, the image inpainting can be formulated as a constrained optimization problem. The Mumford-Shah model is a famous and effective model to solve the image inpainting problem. In this paper, we propose an adaptive image inpainting method based on multiscale parameter estimation for the modified Mumford-Shah model. In the experiments, we will handle the comparison with other similar inpainting methods to prove that the combination of classic model such the modified Mumford-Shah model and the multiscale parameter estimation is an effective method to solve the inpainting problem.

Keywords:
image inpainting, Mumford-Shah model, modified Mumford-Shah model, regularization, Euler-Lagrange equation, inverse gradient, multiscale.

Citation:
Thanh DNH, Prasath VBS, Son NV, Son NV, Hieu LM. An adaptive image inpainting method based on the modified Mumford-Shah model and multiscale parameter estimation. Computer Optics 2019; 43(2): 251-257. DOI: 10.18287/2412-6179-2019-43-2-251-257.

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