Image selective restoration using multi-scale variational decomposition |
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| Affiliation: | 1. Department of Information Management, National Central University, Taiwan;2. Department of Computer Science and Information Engineering, Asia University, Taiwan;3. Department of Information and Computer Engineering, Chung Yuan Christian University, Taiwan;1. Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Bangladesh;2. Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Bangladesh;1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;2. Guiling University of Electronic Technology, GuiLing, Guangxi, China;1. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. University Multimedia, Malaysia;3. The Chinese University of Hong Kong, Hong Kong;1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Information, Qilu University of Technology, Jinan 250353, China |
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| Abstract: | In this paper we propose a multi-scale variational decomposition model for image selective restoration. Firstly, we introduce a single-parameter (BV, G, L2) variational decomposition functional and theoretically analyze the relationship between the parameter and the scale of image features. And then, by replacing the fixed scale parameter with a varying sequence in the single-parameter decomposition functional, we obtain the multi-scale variational decomposition which can decompose the input image into a series of image slices of different scales. Furthermore, we show some properties and prove the convergence of the multi-scale decomposition. Finally, we introduce an alternating and iterative method based on Chambolle’s projection algorithm to numerically solve the multi-scale variational decomposition model. Experiments are conducted on both synthetic and real images to demonstrate the effectiveness of the proposed multi-scale variational decomposition. In addition, we use the multi-scale variational decomposition to achieve image selective restoration, and compare it with several state-of-the-art models in denoising application. The numerical results show that our model has better performance in terms of PSNR and SSIM indexes. |
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| Keywords: | Total variation Variational decomposition Image restoration Scale |
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