Advertisement

Image Super Resolution Reconstruction Using Iterative Adaptive Regularization Method and Genetic Algorithm

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)

Abstract

Super resolution is a technique to obtain high resolution images from several degraded low-resolution images. This has got attention in the research society because of its wide use in many fields of science and technology. Even though many methods exist for super resolution, adaptive regularization method is preferred because of its simplicity and the constraints used to get better image restoration result. In this paper first adaptive algorithm is considered to restore better edge and texture of image. Further Genetic algorithm is used to smooth the noise and better frequency addition into the image to get an optimum super resolution image.

Keywords

Peak signal to noise ratio (PSNR) Regularization  Low/high resolution (LR:HR) Genetic algorithm (GA) 

References

  1. 1.
    Bing, T., Qing, X., Xun, G., Shuai, X.: Super-resolution image reconstruction technology development status of the information engineering university 4(4) (2003)Google Scholar
  2. 2.
    Borman, S., Stevenson, R.: Spatial Resolution Enhancement of Low-resolution Image Sequences a Comprehensive Review with Directions for Future Research [online]. http://citeseer.nj.nec.com
  3. 3.
    Gold, W.W.: Adaptive regularized image restoration (Ph.D. thesis). National Defense University, Washington (2006)Google Scholar
  4. 4.
    Geman, D., Yang, C.: Nonlinear image recovery with half-quadratic regularization. IEEE Trans. Image Process. 4(7), 932–946 (1995)CrossRefGoogle Scholar
  5. 5.
    Kang, M.G., Katsaggelos, A.K., Schafer, R.W.: A regularized iterative image restoration algorithm. IEEE Trans. Signal Process. 39(4) (1991)Google Scholar
  6. 6.
    Tsai, R.Y., Huang, T.S.: Multiframe image restoration and registration. Adv. Comput. Vis. Image Process. Greenwich 1(2), 317–339 (1984)Google Scholar
  7. 7.
    Belge, M., Kilmer, M.E., Miller, E.L.: Wavelet domain image restoration with adaptive edge-preserving regularization. IEEE Trans. Image Process. 9(4), 597–608 (2000)CrossRefMATHGoogle Scholar
  8. 8.
    Panda, S.S. : (IJAEST) International Journal of Advance Engineering Science and Technologies, 11(Issue No. 1), pp. 008–014Google Scholar
  9. 9.
    Yugeng, X., Tianyou, C., Weimin, Y. : Summarization of genetic algorithm. Control Theory Appl. 697–708 (1996)Google Scholar
  10. 10.
    Efrat, N., et al.: Accurate Blur Models versus image priors in single image super-resolution. In: IEEE International Conference on Computer Vision (ICCV). IEEE (2013)Google Scholar
  11. 11.
    Dai, S.S., et al.: Super-resolution reconstruction of images based on uncontrollable microscanning and genetic algorithm. Optoelectron. Lett. 10, 313–316 (2014)CrossRefGoogle Scholar
  12. 12.
    Ling, F., et al.: Post-processing of interpolation-based super-resolution mapping with morphological filtering and fraction refilling. Int. J. Remote Sens. 35(13), 5251–5262 (2014)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.AMET UniversityChennaiIndia
  2. 2.Roland Institute of TechnologyBerhampurIndia

Personalised recommendations