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Poissonian blurred image deconvolution by framelet-based local minimal prior

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Abstract

Image production tools do not always create a clear image, noisy and blurry images are sometimes created. Among these cases, Poissonian noise is one of the most famous noises that appear in medical images and images taken in astronomy. In recent years, various methods have been proposed to improve the quality of the image that has been lost due to this noise. For example, we can refer to methods that use fractional-order and second-order total variation priors or proximal thresholding. In this paper, in the first step, based on framelet transform, a local minimal prior is introduced, and in the next step, this tool together with fractional calculation is used for Poissonian blurred image deconvolution. The framelet transform domain of images usually have sparse representations. It is also well known that the use of framelet transfer has a proper effect on the edges of the restored image. Also, In this study, both blind and nonblind problems are considered. To evaluate the performance of the presented model, several images such as real images have been investigated. Various tools are used to study the efficiency of the proposed method such as PSNR and SSIM. The proposed method is compared with the existing methods such as fractional - order and second-order total variation. The simulation results show the appropriate representation of the proposed method in solving this type of problem.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Notes

  1. https://www.mathworks.com/help/images/ref/fspecial.html

  2. http://sipi.usc.edu/database/

  3. https://www.imageprocessingplace.com/index.htm

  4. http://www.astrospider.com/index.htm

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Acknowledgements

We would like to thank the reviewers for their helpful comments and suggestions which greatly improve the quality of the paper. We also thank Dr. Yu Shi for providing some codes.

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

    Reza Parvaz

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  1. Reza Parvaz
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Correspondence to Reza Parvaz.

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Parvaz, R. Poissonian blurred image deconvolution by framelet-based local minimal prior. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17733-4

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