Image Super Resolution In View of Sparse Representation

Authors

  • Keerthi PN MTech, Department of CSE, S.J.M Institute of Technology, Chitradurga
  • Shruthi MK Associate Professor, Department of CSE, S.J.M Institute of Technology, Chitradurga

Keywords:

super resolution, ridge regression, sparse representation, gradient histogram

Abstract

Sparse representation has attracted it interests in the field of image resolution. On small images with certain constraints the sparsity based methods enforce sparse coding. For the observed low resolution images it has certain limitations on small scale and different scales for image sparse representation. In this paper a joint super resolution framework has been proposed to improve sparsity based image performances. The algorithm proposed here optimizes the problem for high resolution image recovery. Both the ridge regression and the gradient histogram is incorporated to solve the problem.

References

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Published

2025-11-11

How to Cite

[1]
P. Keerthi and M. Shruthi, “Image Super Resolution In View of Sparse Representation”, Int. J. Comp. Sci. Eng., vol. 4, no. 3, pp. 97–100, Nov. 2025.

Issue

Vol. 4 No. 3 (2016): IJCSE Special Issue May Edition

Section

Research Article

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