High dynamic range image tone mapping based on asymmetric model of retinal adaptation

Dae Hong Lee, Ming Fan, Seung Wook Kim, Mun Cheon Kang, Sung Jea Ko

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


Global tone mapping operators using the symmetrical retinal response model to light tend to produce a low dynamic range (LDR) image that exhibits loss of details of its corresponding high dynamic range (HDR) image in a bright or dark area. In this paper, we introduce a new asymmetric sigmoid curve (ASC) based on the model of retinal adaptation encompassing symmetrical S-shaped curve, and present two global tone mapping operators by using the ASC. In the proposed method, an ASC-based tone mapping function is obtained by using a well-known classic photography technique, called zone system. In addition, a contrast-enhancing tone mapping function is introduced by formulating a bi-criteria optimization problem with the luminance histogram of an input HDR image and the ASC-based mapping function. Experimental results demonstrate that the proposed method enhances the global contrast while preserving image details in the tone-mapped LDR image. Moreover, the objective assessment results using an image quality metric indicate that the proposed method shows a high performance to state-of-the-art global tone mapping operators.

Original languageEnglish
Pages (from-to)120-128
Number of pages9
JournalSignal Processing: Image Communication
Publication statusPublished - 2018 Oct

Bibliographical note

Funding Information:
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) ( 2017-0-00250 , Intelligent Defense Boundary Surveillance Technology Using Collaborative Reinforced Learning of Embedded Edge Camera and Image Analysis).

Publisher Copyright:
© 2018


  • High dynamic range image
  • Perceptual image processing
  • Retina response model
  • Tone mapping operator

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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