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An improved image quality algorithm for exemplar-based image inpainting

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Abstract

Image inpainting is a common technique for repairing image regions that are scratched or damaged. This process involves reconstructing damaged parts and filling-in regions in which data/colour information is missing. There are many potential applications for image inpainting, such as repairing old images, repairing scratched images, removing unwanted objects, and filling-in missing areas. This paper develops an exemplar-based algorithm, one of the most important and popular image inpainting techniques, to fill-in missing regions caused by removing unwanted objects, image compression, scratches, or image transformation via the Internet. The proposed algorithm includes two phases of searching to select the best-matching information. In the first phase, the searching mechanism uses the entire image to find and select the most similar patches using the Euclidean distance. The second phase measures the distance between the location of the selected patches and the location of the patch to be filled. The performance of the proposed approach is evaluated through comprehensive experiments on several well-known images used in this area of research. The experimental results demonstrate the superior performance of the proposed approach over some state-of-the-art approaches in terms of quality in terms of both objective (using the peak signal-to-noise ratio (PSNR) as well as the structural similarity index method (SSIM)) and subjective (i.e., visual) measures.

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Authors and Affiliations

  1. Department of Information Technology, College of Commerce, University of Sulaimani, Sulaymaniyah, Iraq

    Alan Anwer Abdulla

  2. Department of Computer, College of Science, University of Sulaimani, Sulaymaniyah, Iraq

    Mariwan Wahid Ahmed

Authors
  1. Alan Anwer Abdulla
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  2. Mariwan Wahid Ahmed
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Correspondence to Alan Anwer Abdulla.

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Abdulla, A.A., Ahmed, M.W. An improved image quality algorithm for exemplar-based image inpainting. Multimed Tools Appl 80, 13143–13156 (2021). https://doi.org/10.1007/s11042-020-10414-6

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  • DOI: https://doi.org/10.1007/s11042-020-10414-6

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