Multimedia Tools and Applications

, Volume 77, Issue 2, pp 2467–2484 | Cite as

Joint optimization and perceptual boosting of global and local contrast for efficient contrast enhancement

  • Zhigang LingEmail author
  • Guoliang Fan
  • Yan Liang
  • Junyi Zuo


Contrast enhancement which aims to increase the contrast of an image with low dynamic range, has been widely studied and exploited. In spite of the great success of many contrast enhancement algorithms, they still have difficulty in achieving both global and local contrast enhancement so that some over-enhancement, under-enhancement or even halo artifacts are often produced in complex images. This paper proposes a simple but efficient contrast enhancement method which may achieve both global and local contrast enhancement and perceptually suppress the above-mentioned problems. A cost function integrating global enhancement with local contrast enhancement is firstly constructed to pose image enhancement as an optimization problem. Then, two key steps are involved in solving for an optimal solution, the just-noticeable difference (JND) model is introduced to perceptually determine maximum local gains and neighbouring gray-level difference for local and global contrast enhancement, respectively, and an adaptive parameter regularization method is invoked to further suppress over-enhancement and halo artifacts. The experimental results on many images both qualitatively and quantitatively demonstrate our algorithm can robustly provide better visual quality in global and local contrast compared to a selection of other well-known state-of-the-art algorithms.


Contrast enhancement Joint optimization of local and global contrast Perceptual contrast boosting Just-noticeable difference Adaptive parameter regularization 



This work was supported by National Natural Science Foundation of China (Grant No. 61471166, 61473227 and 61374023) and Natural Science Foundation of Hunan Province (CN) (14JJ2052).


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Zhigang Ling
    • 1
    Email author
  • Guoliang Fan
    • 2
  • Yan Liang
    • 3
  • Junyi Zuo
    • 4
  1. 1.College of Electrical and Information EngineeringHunan UniversityChangshaChina
  2. 2.School of Electrical and Computer EngineeringOklahoma State UniversityStillwaterUSA
  3. 3.School of AutomationNorthwestern Polytechnical UniversityXi’anChina
  4. 4.School of AstronauticsNorthwestern Polytechnical UniversityXi’anChina

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