Optimized contrast enhancement for real-time image and video dehazing

Jin Hwan Kim, Won Dong Jang, Jae Young Sim, Chang-Su Kim

Research output: Contribution to journalArticlepeer-review

461 Citations (Scopus)


A fast and optimized dehazing algorithm for hazy images and videos is proposed in this work. Based on the observation that a hazy image exhibits low contrast in general, we restore the hazy image by enhancing its contrast. However, the overcompensation of the degraded contrast may truncate pixel values and cause information loss. Therefore, we formulate a cost function that consists of the contrast term and the information loss term. By minimizing the cost function, the proposed algorithm enhances the contrast and preserves the information optimally. Moreover, we extend the static image dehazing algorithm to real-time video dehazing. We reduce flickering artifacts in a dehazed video sequence by making transmission values temporally coherent. Experimental results show that the proposed algorithm effectively removes haze and is sufficiently fast for real-time dehazing applications.

Original languageEnglish
Pages (from-to)410-425
Number of pages16
JournalJournal of Visual Communication and Image Representation
Issue number3
Publication statusPublished - 2013

Bibliographical note

Funding Information:
The work of J.-H. Kim, W.-D. Jang, and C.-S. Kim was supported partly by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012-011031), and partly by Basic Science Research Program through the NRF of Korea funded by the MEST (No. 2012-0000916). The work of J.-Y. Sim was supported by Basic Science Research Program through the NRF of Korea funded by the MEST (2010-0006595).

Copyright 2013 Elsevier B.V., All rights reserved.


  • Atmospheric light estimation
  • Contrast enhancement
  • Image dehazing
  • Image enhancement
  • Image restoration
  • Optimized dehazing
  • Temporal coherence
  • Video dehazing

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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