Multimedia Tools and Applications

, Volume 56, Issue 2, pp 227–243 | Cite as

A low distortion image enhancement scheme based on multi-resolutions analysis in next generation network

  • Jiao Feng
  • Naixue Xiong
  • Laurence T. Yang
  • Yan Yang


With the advent of Next Generation Network (NGN), services that are currently provided by multiple specific network-centric architectures. NGN provides AAA (Anytime, Anywhere and Always on) access to users from different service providers with consistent and ubiquitous provision of services as necessary. This special issue of NGN includes pervasive, grid, and peer-to-peer computing to provide computing and communication services at anytime and anywhere. In fact, the application of NGN includes digital image processing, multimedia systems/services, and so on. Here we focus on the digital image processing technology in NGN environments. Low-contrast structure and heavy noise in NGN environments can be found in many kinds of digital images, which makes the images vague and uncertainly, especially in x-ray images. As result, some useful tiny characteristic are weakened—which are difficult to distinguish even by naked eyes. Based on the combination of no-linear grad-contrast operator and multi-resolution wavelet analysis, a kind of image enhancement processing algorithm for useful tiny characters is presented. The algorithm can enhance the tiny characters while confine amplifying noise. The analysis of the results shows that local regions of the image are enhanced by using the concept of the grad contrast to make image clearer adaptively. Experiments were conducted on real pictures, and the results show that the algorithm is flexible and convenient.


Image enhancement Medical image Wavelet transform Grad-contrast 



Thank for the support of the National Natural Science Foundation of China (under Grant No.40471101) and support of Research Foundation of Nanjing University of Information Science and Technology.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Jiao Feng
    • 1
  • Naixue Xiong
    • 2
  • Laurence T. Yang
    • 3
  • Yan Yang
    • 4
  1. 1.Computer and Software InstituteNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Department of Computer ScienceGeorgia State UniversityAtlantaUSA
  3. 3.Department of Computer ScienceSt. Francis Xavier UniversityAntigonishCanada
  4. 4.Department of Computer ScienceWestern Illinois UniversityMacombUSA

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