Single image dehazing based on contrast enhancement

Jin Hwan Kim; Jae Young Sim; Chang-Su Kim

doi:10.1109/ICASSP.2011.5946643

Single image dehazing based on contrast enhancement

Jin Hwan Kim, Jae Young Sim, Chang-Su Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

72 Citations (Scopus)

Abstract

A simple and adaptive single image dehazing algorithm is proposed in this work. Based on the observation that a hazy image has low contrast in general, we attempt to restore the original image by enhancing the contrast. First, the proposed algorithm estimates the airlight in a given hazy image based on the quad-tree subdivision. Then, the proposed algorithm estimates the transmission map to maximize the contrast of the output image. To measure the contrast, we develop a cost function, which consists of a standard deviation term and a histogram uniformness term. Experimental results demonstrate that the proposed algorithm can remove haze efficiently and reconstruct fine details in original scenes clearly.

Original language	English
Title of host publication	2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages	1273-1276
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2011.5946643
Publication status	Published - 2011
Event	36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic Duration: 2011 May 22 → 2011 May 27

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Other

Other	36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/Territory	Czech Republic
City	Prague
Period	11/5/22 → 11/5/27

Keywords

Image enhancement
contrast enhancement
image restoration
single image dehazing

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2011.5946643

Cite this

Kim, J. H., Sim, J. Y., & Kim, C-S. (2011). Single image dehazing based on contrast enhancement. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings (pp. 1273-1276). Article 5946643 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2011.5946643

Single image dehazing based on contrast enhancement. / Kim, Jin Hwan; Sim, Jae Young; Kim, Chang-Su.
2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings. 2011. p. 1273-1276 5946643 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, JH, Sim, JY & Kim, C-S 2011, Single image dehazing based on contrast enhancement. in 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings., 5946643, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 1273-1276, 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, Prague, Czech Republic, 11/5/22. https://doi.org/10.1109/ICASSP.2011.5946643

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AB - A simple and adaptive single image dehazing algorithm is proposed in this work. Based on the observation that a hazy image has low contrast in general, we attempt to restore the original image by enhancing the contrast. First, the proposed algorithm estimates the airlight in a given hazy image based on the quad-tree subdivision. Then, the proposed algorithm estimates the transmission map to maximize the contrast of the output image. To measure the contrast, we develop a cost function, which consists of a standard deviation term and a histogram uniformness term. Experimental results demonstrate that the proposed algorithm can remove haze efficiently and reconstruct fine details in original scenes clearly.

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Single image dehazing based on contrast enhancement

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Keywords

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