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IJMLC 2012 Vol.2(5): 711-715 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.220

Contrast Enhancement of Dark Images using Stochastic Resonance in Wavelet Domain

Rajlaxmi Chouhan, C. Pradeep Kumar, Rawnak Kumar, and Rajib Kumar Jha

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.

Index Terms—Metabolic networks, modeling, visualization.

S. N. Kalyankar is with the Department of Chemistry, Yeshwant College, Nanded 431602(M.S.) India (e-mail: drkalyankarsn@ yahoo.com).
N. V. Kalyankar is with the Department of Physics, Yeshwant College, Nanded 431602(M.S.) India (e-mail: drkalyankarnv@rediffmail.com).
M. Thaseen is with Computer Science and Information Technology Department, Yeshwant College, Nanded 431602(M.S.) India (e-mail:mohseena@gmail.com).

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Cite:Rajlaxmi Chouhan, C. Pradeep Kumar, Rawnak Kumar, and Rajib Kumar Jha, "Contrast Enhancement of Dark Images using Stochastic Resonance in Wavelet Domain," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 711-715, 2012.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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