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An adaptive method for image restoration based on high-order total variation and inverse gradient

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Abstract

The total variation (TV) regularization model for image restoration is widely utilized due to its edge preservation properties. Despite its advantages, the TV regularization can obtain spurious oscillations in flat regions of digital images and thus recent works advocate high-order TV regularization models. In this work, we propose an adaptive image restoration method based on a combination of first-order and second-order total variations regularization with an inverse-gradient-based adaptive parameter. The proposed model removes noise effectively and preserves image structures. Due to the adaptive parameter estimation based on the inverse gradient, it avoids the staircasing artifacts associated with TV regularization and its variant models. Experimental results indicate that the proposed method obtains better restorations in terms of visual quality as well as quantitatively. In particular, our proposed adaptive higher-order TV method obtained (19.3159, 0.7172, 0.90985, 0.79934, 0.99838) PSNR, SSIM, MS-SSIM, F-SIM, and P-SIM values compared to related models such as the TV-Bounded Hessian (18.9735, 0.6599, 0.8718, 0.73833, 0.99767), and TV-Laplacian (19.0345, 0.6719, 0.88198, 0.75405, 0.99789).

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Authors and Affiliations

  1. Department of Information Systems, School of Business Information Technology, University of Economics Ho Chi Minh city, Ho Chi Minh city, Vietnam

    Dang N. H. Thanh

  2. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA

    V. B. Surya Prasath

  3. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    V. B. Surya Prasath

  4. Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, 45267, USA

    V. B. Surya Prasath

  5. Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45221, USA

    V. B. Surya Prasath

  6. University of Economics, The University of Danang, Danang, Vietnam

    Le Minh Hieu

  7. Department of Information Security, Institute of Applied Mathematics and Computer Science, Tula State University, Tula, Russia

    Sergey Dvoenko

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  1. Dang N. H. Thanh
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  2. V. B. Surya Prasath
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  3. Le Minh Hieu
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  4. Sergey Dvoenko
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Correspondence to Dang N. H. Thanh.

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Thanh, D.N.H., Prasath, V.S., Hieu, L.M. et al. An adaptive method for image restoration based on high-order total variation and inverse gradient. SIViP 14, 1189–1197 (2020). https://doi.org/10.1007/s11760-020-01657-9

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