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A comparison between the sampling Kantorovich algorithm for digital image processing with some interpolation and quasi-interpolation methods

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  • Costarelli, Danilo
  • Seracini, Marco
  • Vinti, Gianluca

Abstract

In this paper we study the performance of the sampling Kantorovich (S–K) algorithm for image processing with other well-known interpolation and quasi-interpolation methods. The S-K algorithm has been implemented with three different families of kernels: central B-splines, Jackson type and Bochner–Riesz. The above method is compared, in term of PSNR (Peak Signal-to-Noise Ratio) and CPU time, with the bilinear and bicubic interpolation, the quasi FIR (Finite Impulse Response) and quasi IIR (Infinite Impulse Response) approximation. Experimental results show better performance of S-K algorithm than the considered other ones.

Suggested Citation

  • Costarelli, Danilo & Seracini, Marco & Vinti, Gianluca, 2020. "A comparison between the sampling Kantorovich algorithm for digital image processing with some interpolation and quasi-interpolation methods," Applied Mathematics and Computation, Elsevier, vol. 374(C).
    • Handle: RePEc:eee:apmaco:v:374:y:2020:i:c:s0096300320300151
    DOI: 10.1016/j.amc.2020.125046
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    References listed on IDEAS

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    1. Baldinelli, Giorgio & Bianchi, Francesco & Rotili, Antonella & Costarelli, Danilo & Seracini, Marco & Vinti, Gianluca & Asdrubali, Francesco & Evangelisti, Luca, 2018. "A model for the improvement of thermal bridges quantitative assessment by infrared thermography," Applied Energy, Elsevier, vol. 211(C), pages 854-864.
    2. Asdrubali, Francesco & Baldinelli, Giorgio & Bianchi, Francesco & Costarelli, Danilo & Rotili, Antonella & Seracini, Marco & Vinti, Gianluca, 2018. "Detection of thermal bridges from thermographic images by means of image processing approximation algorithms," Applied Mathematics and Computation, Elsevier, vol. 317(C), pages 160-171.
    3. Coroianu, Lucian & Costarelli, Danilo & Gal, Sorin G. & Vinti, Gianluca, 2019. "The max-product generalized sampling operators: convergence and quantitative estimates," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 173-183.
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    Cited by:

    1. Cagini, C. & Costarelli, D. & Gujar, R. & Lupidi, M. & Lutty, G.A. & Seracini, M. & Vinti, G., 2022. "Improvement of retinal OCT angiograms by Sampling Kantorovich algorithm in the assessment of retinal and choroidal perfusion," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    2. Kadak, Ugur, 2022. "Max-product type multivariate sampling operators and applications to image processing," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    3. Kadak, Ugur & Costarelli, Danilo & Coroianu, Lucian, 2023. "Neural network operators of generalized fractional integrals equipped with a vector-valued function," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    4. Danilo Costarelli & Michele Piconi & Gianluca Vinti, 2023. "On the convergence properties of sampling Durrmeyer‐type operators in Orlicz spaces," Mathematische Nachrichten, Wiley Blackwell, vol. 296(2), pages 588-609, February.
    5. Arianna Travaglini & Gianluca Vinti & Giovanni Battista Scalera & Michele Scialpi, 2023. "A Large Scale Analysis for Testing a Mathematical Model for the Study of Vascular Pathologies," Mathematics, MDPI, vol. 11(8), pages 1-19, April.

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