TY - GEN
T1 - Using a hybrid of fuzzy theory and neural network filter for image dehazing applications
AU - Wang, Jyun Guo
AU - Tai, Shen Chuan
AU - Lee, Chin Ling
AU - Lin, Cheng Jian
AU - Lin, Tsung Hung
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/10/31
Y1 - 2016/10/31
N2 - When photographs are being taken in an outdoor environment, the medium in air will cause light attenuation and further reduce image quality, and this impact is especially obvious in a hazy environment. Reduction of image quality results in the loss of information, which renders an image recognition system unable to identify objects in the image. In order to eliminate the hazy effect on images and improve the visual quality, this paper presents an efficient method combining the fuzzy inference system and the neural network filter to solve image dehazing. During dehazing, the fuzzy inference system is adopted to estimate the variations in light attenuation, and the erosion of morphological operation and the neural network filter are used to eliminate the halation and achieve optimization in transmission map refinement. Finally, the brightest 1% of the atmospheric light is utilized to calculate the color vector of atmospheric light to eliminate color cast. The experimental results indicate that the proposed method is superior to other dehazing methods.
AB - When photographs are being taken in an outdoor environment, the medium in air will cause light attenuation and further reduce image quality, and this impact is especially obvious in a hazy environment. Reduction of image quality results in the loss of information, which renders an image recognition system unable to identify objects in the image. In order to eliminate the hazy effect on images and improve the visual quality, this paper presents an efficient method combining the fuzzy inference system and the neural network filter to solve image dehazing. During dehazing, the fuzzy inference system is adopted to estimate the variations in light attenuation, and the erosion of morphological operation and the neural network filter are used to eliminate the halation and achieve optimization in transmission map refinement. Finally, the brightest 1% of the atmospheric light is utilized to calculate the color vector of atmospheric light to eliminate color cast. The experimental results indicate that the proposed method is superior to other dehazing methods.
UR - http://www.scopus.com/inward/record.url?scp=85007198028&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007198028&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2016.7727267
DO - 10.1109/IJCNN.2016.7727267
M3 - Conference contribution
AN - SCOPUS:85007198028
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 692
EP - 697
BT - 2016 International Joint Conference on Neural Networks, IJCNN 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Joint Conference on Neural Networks, IJCNN 2016
Y2 - 24 July 2016 through 29 July 2016
ER -