Reinforced Contrast Adaptation

Hamid R. Tizhoosh, Graham W. Taylor

Research output: Contribution to journalArticlepeer-review


Traditional image enhancement algorithms do not account for the subjective evaluation of human operators. Every observer has a different opinion of an ideally enhanced image. Automated Techniques for obtaining a subjectively ideal image enhancement are desirable, but currently do not exist. In this paper, we demonstrate that Reinforcement Learning is a potential method for solving this problem. We have developed an agent that uses the Q-learning algorithm. The agent modifies the contrast of an image with a simple linear point transformation based on the histogram of the image and feedback it receives from human observers. The results of several testing sessions have indicated that the agent performs well within a limited number of iterations.

Original languageEnglish (US)
Pages (from-to)377-392
Number of pages16
JournalInternational Journal of Image and Graphics
Issue number3
StatePublished - Jul 1 2006


  • contrast
  • image enhancement
  • Q-learning
  • Reinforcement learning
  • subjectivity

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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