Transmission map estimation of weather-degraded images using a hybrid of recurrent fuzzy cerebellar model articulation controller and weighted strategy

Jyun-Guo Wang; Shen-Chuan Tai; Cheng-Jian Lin

doi:10.1117/1.OE.55.8.083104

12 August 2016 Transmission map estimation of weather-degraded images using a hybrid of recurrent fuzzy cerebellar model articulation controller and weighted strategy

Jyun-Guo Wang, Shen-Chuan Tai, Cheng-Jian Lin

Author Affiliations +

Optical Engineering, Vol. 55, Issue 8, 083104 (August 2016). https://doi.org/10.1117/1.OE.55.8.083104

Abstract

This study proposes a hybrid of a recurrent fuzzy cerebellar model articulation controller (RFCMAC) and a weighted strategy for solving single-image visibility in a degraded image. The proposed RFCMAC model is used to estimate the transmission map. The average value of the brightest 1% in a hazy image is calculated for atmospheric light estimation. A new adaptive weighted estimation is then used to refine the transmission map and remove the halo artifact from the sharp edges. Experimental results show that the proposed method has better dehazing capability compared to state-of-the-art techniques and is suitable for real-world applications.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Jyun-Guo Wang, Shen-Chuan Tai, and Cheng-Jian Lin "Transmission map estimation of weather-degraded images using a hybrid of recurrent fuzzy cerebellar model articulation controller and weighted strategy," Optical Engineering 55(8), 083104 (12 August 2016). https://doi.org/10.1117/1.OE.55.8.083104

Published: 12 August 2016

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Cited by 3 scholarly publications.

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KEYWORDS

Fuzzy logic

Image analysis

Atmospheric particles

Air contamination

Visibility

Atmospheric modeling

Optical engineering

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