Call for Paper

CAE solicits original research papers for the October 2021 Edition. Last date of manuscript submission is September 30, 2021.

Read More

Enhancement of Image Resolution: A Survey

Dimple Mittal, Husanbir Singh Pannu. Published in Image Processing.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Dimple Mittal, Husanbir Singh Pannu
10.5120/cae2016652322

Dimple Mittal and Husanbir Singh Pannu. Enhancement of Image Resolution: A Survey. Communications on Applied Electronics 5(6):31-33, July 2016. BibTeX

@article{10.5120/cae2016652322,
	author = {Dimple Mittal and Husanbir Singh Pannu},
	title = {Enhancement of Image Resolution: A Survey},
	journal = {Communications on Applied Electronics},
	issue_date = {July 2016},
	volume = {5},
	number = {6},
	month = {Jul},
	year = {2016},
	issn = {2394-4714},
	pages = {31-33},
	numpages = {3},
	url = {http://www.caeaccess.org/archives/volume5/number6/628-2016652322},
	doi = {10.5120/cae2016652322},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The High Resolution images have great importance in various fields, such as astronomy, medical imaging, agricluture, video surveillance, etc. These High Resolution images are useful to get the required details which are important for analysis in many applications. This paper investigates mainly on various modern existing methods of super resolution that and putting it all together for a literature survey. Scope of this study mainly focuses on the different available techniques of image processing to get high resolution images for extracting the meticulous details.

References

  1. Wang, Z. 2015. Ding Liu, Jianchao Yang, Wei han. Deep Networks for Image Super-Resolution with Sparse Prior. IEEE (Dec 2015), 370-378.
  2. Dinh-Hoan, Luong, M., Dibos, F., Rocchisani, J.M. 2014. Novel Example-Based Method for Super-Resolution and Denoising of Medical Images. IEEE Trans. Signal Processing, April 2014.
  3. F.Russo. 2002. An image enhancement technique combining sharpening and noise reduction. IEEE Transactions on Instrumentation and Measurement. Aug 2002, 824-828.
  4. Zheng, H., Bouzerdoum, A., Phung, S.L. 2015 Depth image super-resolution using internal and external information. IEEE International Conference on Acoustics, Speech and Signal Processing, Aug 2015.
  5. Chang, H., Yeung, D.-Y., Xiong, Y. 2004 Super-resolution through neighbor embedding. CVPR.
  6. Deng, G. 2011. A Generalized Unsharp Masking Algorithm. IEEE Transactions on Image Processing, 1249-1261.
  7. Okuhata, H., Omaki, R.Y. 2013. Implementation of super-resolution scalar for Full HD and 4K video. IEEE Third International Conference on Consumer Electronics, Sept 2013.
  8. Aftab, H., Mansoor, A.B., Asim, M. 2008. A new single image interpolation technique for super resolution. IEEE International, , Dec 2008, 592-596.
  9. Keys, R. 1981. Cubic convolution interpolation for digital image processing, in Acoustics, Speech and Signal Processing. IEEE Transactions., Dec 1981, 1153-1160.
  10. Senn, A.R. 2008. Model-based super-resolution for MRI. IEEE, Aug 2008, , 430-434.
  11. Yang, J., Wright, Huang, J., Yi Ma, T.S. 2010. Image Super-Resolution Via Sparse Representation in Image Processing. IEEE Transactions, Nov 2010, 2861-2873.
  12. Dong, W., Fu, F. 2016 Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation. IEEE Transactions On Image Processing, May 2016.
  13. Qi, C., Zhu G., Ji X., Zhao, L. Image super resolution reconstruction algorithm based on sparse representation and the UV chroma processing.
  14. Progress in Informatics and Computing (PIC). 2014 International Conference on, IEEE, May 2014, 368-372.
  15. Ramos, V.A., and Ponomaryov, V. 2016. Sparse Representation to Solve the Problem of Image Super-Resolution, IEEE Conference Proceeding Conielecomp, Feb , 2016.

Keywords

SSIM, LISTA, HR images