Home > Archive > 2012 > Volume 2 Number 5 (Oct. 2012) >
IJML 2012 Vol.2(5): 711-715
doi: 10.7763/IJMLC.2012.V2.220

Contrast Enhancement of Dark Images using Stochastic Resonance in Wavelet Domain

S. N. Kalyankar1,2 , N. V. Kalyankar1,2 , Mohseena Thaseen3

  • 1Department of Chemistry, Yeshwant College,Nanded 431602(M.S.) India.
  • 2Department of Physics, Yeshwant College,Nanded 431602(M.S.) India
  • 3Computer Science and Information TechnologyDepartment, Yeshwant College, Nanded 431602(M.S.) India

Abstract

Metabolism is the set of biochemical reactions occurring in living organisms. Metabolites are usually small molecules like glucose, amino acids etc. These biochemical inter-conversions are generally catalyzed by enzymes. The sequencing of genomes and development of functional genomics make it now possible to reconstruct and understand the structure and function of metabolic networks at large scale. New computational tools and biological concepts are being developed to understand these metabolic networks more precisely. Here attempts were made for reconstruction, visualization, and graph representation of metabolic networks for structural analysis i.e. connectivity and centrality analyses, modularity and decomposition of the networks to fundamental level. Reconstruction, visualization, and graphical representation of glycolysis for structural analysis and decomposition of the network to fundamental is done as an example of basic metabolic network in cells. The methods and concepts presented deals with static properties and functions of glycolysis and more complex networks can be represented following similar methods.

Keywords

  • Metabolic networks
  • modeling
  • visualization
221-L485

How to Cite

Copied

S. N. Kalyankar, N. V. Kalyankar, and Mohseena Thaseen, "Contrast Enhancement of Dark Images using Stochastic Resonance in Wavelet Domain," International Journal of Machine Learning, vol. 2, no. 5, pp. 711-715, 2012. https://doi.org/10.7763/IJMLC.2012.V2.220

Copyright & License

Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Article Metrics in Dimensions

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • DOI: 10.18178/IJML
  • Frequency: Quarterly
  • Average Days to Accept: 68 days
  • Acceptance Rate: 27%
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • DOI: 10.18178/IJML
  • Abstracing/Indexing: Google Scholar, Crossref, ProQuest, Electronic Journals Library, CNKI.
  • E-mail: editor@ijml.org
  • APC: 500USD