Compressive Sensing based Image Reconstruction Algorithms – A Performance Review

  • Unique Paper ID: 144669
  • Volume: 4
  • Issue: 1
  • PageNo: 367-371
  • Abstract:
  • Compressive Sensing (CS) is a powerful high resolution image modelling technique which has been successfully applied in digital image processing and various computer vision applications. This paper will portrait the current image reconstruction algorithms, their drawbacks and an overview of performance metrics such as image quality and PSNR improvements. The performance metric modelling is carried to trade-off choice of reconstruction algorithm with the quality of the reconstructed image considering various applications.
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Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{144669,
        author = {Dr. Vasudha S and Anupama H and Dr. S Shanthala and Dr. H.R. Mahadevaswamy},
        title = {Compressive Sensing based Image Reconstruction Algorithms – A Performance Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {1},
        pages = {367-371},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144669},
        abstract = {Compressive Sensing (CS) is a powerful high resolution image modelling technique which has been successfully applied in digital image processing and various computer vision applications. This paper will portrait the current image reconstruction algorithms, their drawbacks and an overview of performance metrics such as image quality and PSNR improvements. The performance metric modelling is carried to trade-off choice of reconstruction algorithm with the quality of the reconstructed image considering various applications.},
        keywords = {Compressive Sensing, Matching Pursuit, Basis Pursuit, Greedy algorithm, Sparse representation, Smoothed L0 norm},
        month = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 4
  • Issue: 1
  • PageNo: 367-371

Compressive Sensing based Image Reconstruction Algorithms – A Performance Review

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