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Semi-blind image deblurring based on framelet prior

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Abstract

The problem of image blurring is one of the most studied topics in the field of image processing. Image blurring is caused by various factors such as hand or camera shake. To restore the blurred image, it is necessary to know information about the point spread function (PSF). And because in the most cases it is not possible to accurately calculate the PSF, we are dealing with an approximate kernel. In this paper, the semi-blind image deblurring problem is studied. Due to the fact that the model of the deblurring problems is an ill-conditioned problem, it is not possible to solve this problem directly. One of the most efficient ways to solve this problem is to use the total variation (TV) method. In the proposed algorithm, by using the framelet transform and fractional calculations, the TV method is improved. The proposed method is used on different types of images and is compared with existing methods with different types of tests.

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Data Availability

Data sharing is not applicable to this article as no new data were created or analyzed in this study. Declaration Conflicts of interest Authors declare that they have no conflict of interest.

Notes

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Acknowledgements

We would like to thank the referees and the editor for their valuable comments to improve the manuscript.

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The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Department of Mathematics, University of Mohaghegh Ardabili, Ardabil, 56199-11367, Iran

    M. Zarebnia & R. Parvaz

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  1. M. Zarebnia
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  2. R. Parvaz
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M.Zarebnia and R.Parvaz wrote the main manuscript text, prepared figures and tables and reviewed the manuscript.

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Correspondence to M. Zarebnia.

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Zarebnia, M., Parvaz, R. Semi-blind image deblurring based on framelet prior. SIViP 18, 2509–2519 (2024). https://doi.org/10.1007/s11760-023-02926-z

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