Image Fusion Technique for Remote Sensing Image Enhancement

  • B. Saichandana
  • S. Ramesh
  • K. Srinivas
  • R. Kirankumar
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)

Abstract

Remote sensing image enhancement algorithm based on image fusion is presented in this paper, in-order to resolve problems of poor contrast and sharpness in degraded images. The proposed method consists of two essentials: the first, the techniques of image sharpening, dynamic histogram equalization and fuzzy enhancement technique were respectively applied to the same degraded image. Second, these obtained three images, which individually preserved the enhancement effect of either of these techniques, are fused into a single image via different fusion rules. This method enhances the contrast well in the remote sensing image without introducing severe side effects, such as washed out appearance, checkerboard effects etc., or undesirable artifacts. The experiment results indicate that the proposed algorithm integrates the merits of dynamic histogram equalization, fuzzy enhancement and sharpening effectively and achieves a considerable efficiency in the enhancement of degraded images exhibiting both blurred details and low contrast. The qualitative and quantitative performances of the proposed method are compared with other methods producing better quality enhanced image.

Keywords

Image Fusion Image Enhancement Histogram Equalization Fuzzy Image Enhancement Image Processing 

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References

  1. 1.
    Hanmandlu, M., Jha, D.: An Optimal Fuzzy System for Color Image Enhancement. IEEE Transactions on Image Processing 15, 2956–2966 (2006)CrossRefGoogle Scholar
  2. 2.
    Pohl, C., Van Genderen, J.L.: Multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing 19(5), 823–854 (1998)CrossRefGoogle Scholar
  3. 3.
    Pei, L., Zhao, Y., Luo, H.: Application of Wavelet-based Image Fusion in Image Enhancement. In: 2010 3rd International Congress on Image and Signal Processing (2010)Google Scholar
  4. 4.
    Abdullah-Al-Wadud, M., Kabir, H., AliAkber Dewan, M., Chae, O.: A Dynamic Histogram Equalization for Image Contrast Enhancement. IEEE (2007)Google Scholar
  5. 5.
    Wang, Y., Chen, Q., Zhang, B.: Image enhancement based on equal area dualistic subimage histogram equalization method. IEEE Tran. Consumer Electron. 45(1), 68–75 (1999)Google Scholar
  6. 6.
    Tao, G.Q., Li, D.P., Lu, G.H.: Study on Image Fusion Based on Different Fusion Rules of Wavelet Transform. Infrared and Laser Engineering 32(2), 173–176 (2003)Google Scholar
  7. 7.
    Qiang, Z.X., Peng, J.X., Wang, H.Q.: Remote Sensing Image Fusion Based on Local Deviation of Wavelet Transform. Huazhong Univ. of Sci. & Tech. (Nature Science Edition) 31(6), 89–91 (2003)Google Scholar
  8. 8.
    Liu, C.C., Hu, S.B., Yang, J.H., Guo, X.: A Method of Histogram Incomplete Equalization. Journal of Shandong University (Engineering Science) 33(6), 661–664 (2003)Google Scholar
  9. 9.
    Hasikin, K., Isa, N.A.M.: Enhancement of low contrast image using fuzzy set theory. In: 2012 14th International Conference on Modeling and Simulation. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • B. Saichandana
    • 1
  • S. Ramesh
    • 2
  • K. Srinivas
    • 3
  • R. Kirankumar
    • 4
  1. 1.GITAM Institute of TechnologyGITAM UniversityVisakhapatnamIndia
  2. 2.Sri Vahini Institute of Science and TechnologyTiruvuruIndia
  3. 3.Siddhartha Engineering CollegeVijayawadaIndia
  4. 4.Krishna UniversityMachilipatnamIndia

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