Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Image De-blurring and Supper-Resolution by Adaptive Sparse Domain Selection and Regularization

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Monika Banafar, Deepak Kourav
10.5120/ijca2016909645

Monika Banafar and Deepak Kourav. Image De-blurring and Supper-Resolution by Adaptive Sparse Domain Selection and Regularization. International Journal of Computer Applications 143(4):1-3, June 2016. BibTeX

@article{10.5120/ijca2016909645,
	author = {Monika Banafar and Deepak Kourav},
	title = {Image De-blurring and Supper-Resolution by Adaptive Sparse Domain Selection and Regularization},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {143},
	number = {4},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {1-3},
	numpages = {3},
	url = {http://www.ijcaonline.org/archives/volume143/number4/25062-2016909645},
	doi = {10.5120/ijca2016909645},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The method of total variation is used as a significant and competent image prior model in the regularization based area of image processing. However, as the model showing total variation supports a piecewise steady explanation, this process is classify below high intensity noise in the level areas of the picture is often poor, and a small number of pseudo edges are produced. Under this work we build up a spatially adaptive total variation model. Initially, we extract the spatial data based on each and every pixel, then two filtering process are combined to control the collision of pseudo edges. It also includes, the spatial information weight is build and classified with k-means clustering, and the cluster controls the center value of regularization strength in every region. The tentative results, of both simulated as well as genuine datasets, exhibit that the projected methodology can effectively diminish the pseudo edges formed by the total variation regularization in the flat areas, and maintain the limited smoothness of the HR images. The proposed region based spatial information adaptive variation model can effectively reduce the cause of noise on the spatial data extraction and maintain strength with changes in the noise intensity in the SR process as compare to traditional pixel based spatial information adaptive methodology.

References

  1. H. Greenspan, “Super-resolution in medical imaging,” Comput. J,ol. 52, no. 1, pp. 43–63, Jan. 2009.
  2. X. Huang, L. Zhang, and P. Li, “Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery,”IEEE Trans. Geosci. Remote Sens. Lett., vol. 4, no. 2, pp. 260–264,Apr. 2007.
  3. X. Huang and L. Zhang, “An adaptive mean-shift analysis approach for object extraction and classification from urban hyper spectral imagery,”IEEE Trans. Geosci. Remote Sens., vol. 46, no. 12, pp. 4173–4185,Dec. 2008.
  4. L. Zhang, H. Zhang, H. Sheen, and P. Li, “A super-resolution reconstruction algorithm for surveillance images,” Signal Process., vol. 90, no. 3, pp. 848–859, 2010.
  5. R. Tsai and T. Huang, “Multiple frame image restoration and registration,” Adv. Comput.Vis. Image Process., vol. 1, no. 2, pp. 317–339, 1984.
  6. S. Kim, N. Bose, and H. Valenzuela, “Recursive reconstruction of high resolution image from noisy under sampled multi frames,” IEEE Trans. Acoust., Speech, Signal Process., vol. 38, no. 6, pp. 1013–1027, Jun. 1990.
  7. S. Kim and W. Su, “Recursive high-resolution reconstruction of blurred multiframe images,” IEEE Trans. Image Process., vol. 2, no. 4, pp. 534–539, Oct. 1993.
  8. H. Ur and D. Gross, “Improved resolution from sub-pixel shifted pictures,” Comput. Vis. Graph., Graph. Models Image Process., vol. 54, no. 2, pp. 181–186, 1992.
  9. M. Alam, J. Bognar, R. Hardie, and B. Yasuda, “Infrared image registration and high-resolution reconstruction using multiple translationally shifted aliased video frames,” IEEE Trans. Instrum. Meas., vol. 49, no. 5, pp. 915–923, Oct. 2000.
  10. B. Tom and A. Katsaggelos, “Reconstruction of a high-resolution image by simultaneous registration, restoration, and interpolation of low resolution images,” in Proc. IEEE Int. Conf. Image Process., vol. 2. Washington, DC, USA, 1995, pp. 539–542.
  11. R. Schultz and R. Stevenson, “Extraction of high-resolution frames from video sequences,” IEEE Trans. Image Process., vol. 5, no. 6, pp. 996–1011, Jun. 1996.
  12. S. Belekos, N. Galatsanos, and A. Katsaggelos, “Maximum a posteriori video super-resolution using a new multichannel image prior,” IEEE Trans. Image Process., vol. 19, no. 6, pp. 1451–1464, Jun. 2010.

Keywords

Total variation, regional spatially adaptive, Super resolution, High resolution, Majorization-minimization.