Contrast Enhancement Using Sensitivity Model-Based Sigmoid Function

Seung Park, Yong Goo Shin, Sung Jea Ko

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


For indirect contrast enhancement, researchers have proposed various transformation functions based on histogram equalization and gamma correction. However, these transformation functions tend to result in over-enhancement artifacts such as noise amplification, mean brightness change, and detail loss. To overcome the limitations of conventional transformation functions, this paper introduces a novel sigmoid function based on the contrast sensitivity of human brightness perception. In the proposed method, the contrast sensitivity of the human retina is modeled as an exponential function of the log-intensity, and a transformation function is derived using the sensitivity model as the exponent of Steven's power law. We also present a parameter optimization method that maintains the mean brightness of the input image and stretches the image histogram while minimizing information loss. Experimental results demonstrate that the proposed method has low computational complexity and outperforms the state-of-The-Art methods in terms of contrast enhancement performance, mean brightness preservation, and detail preservation.

Original languageEnglish
Article number8891721
Pages (from-to)161573-161583
Number of pages11
JournalIEEE Access
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This work was supported in part by the Institute for Information and Communications Technology Promotion (IITP) through the Ministry of Science and ICT (MSIT) of Korea Government under Grant 2017-0-00250, and in part by the Intelligent Defense Boundary Surveillance Technology Using Collaborative Reinforced Learning of Embedded Edge Camera and Image Analysis.

Publisher Copyright:
© 2013 IEEE.


  • Contrast enhancement
  • Steven's power law
  • sensitivity model-based sigmoid function

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering


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