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Feature enhancing image inpainting through adaptive variation of sparse coefficients

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Abstract

An obvious problem in image inpainting is the mismatch of features and intensity of the restored region with the neighbor source regions. This paper has addressed the problem of that mismatching and presents a novel inpainting method based on the sparse representation technique that employs an adaptive intensity and feature-based target patch-restoration. The mismatch of texture is handled by utilizing a feature-based sparsity, and the adaptive intensity method provides a suitable matching of intensity labels. Moreover, the patch-based sparse representation avoids the excessive extension of texture information and prevents false matching. Therefore, the uniformity of intensity distribution and structural/textural continuation improves the quality of the restored image. The experimental results are tested on several test images with different types of scratches, i.e., missing patches. The performance of the proposed method is compared with other recent methods by computing PSNR, SSIM, and blur metric. The quantitative and subjective evaluations of the output image confirm the superiority of the proposed model.

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Authors and Affiliations

  1. Department of Physics, National Institute of Technology Durgapur, Durgapur, West Bengal, 713209, India

    Arghya Pathak, Jayashree Karmakar & Mrinal Kanti Mandal

  2. Department of CSE, National Institute of Technology Durgapur, Durgapur, West Bengal, 713209, India

    Debashis Nandi

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  1. Arghya Pathak
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  2. Jayashree Karmakar
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  3. Debashis Nandi
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  4. Mrinal Kanti Mandal
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Correspondence to Mrinal Kanti Mandal.

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Pathak, A., Karmakar, J., Nandi, D. et al. Feature enhancing image inpainting through adaptive variation of sparse coefficients. SIViP 17, 1189–1197 (2023). https://doi.org/10.1007/s11760-022-02326-9

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  • DOI: https://doi.org/10.1007/s11760-022-02326-9

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