TY - GEN
T1 - Heavy haze removal in a learning framework
AU - Chen, Jie
AU - Chau, Lap Pui
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/27
Y1 - 2015/7/27
N2 - Extreme weather hazards happens more often these days due to climate changes and increased human industrial activities, and one of most notorious of them is haze. State-of-the-art haze removal methods generally work well with light haze conditions, however when haze gets heavier, the physical model tend to produce over-shadowed, noisy, and color distorted restorations. A new physical model has been proposed in this paper for heavy haze weathers. An airlight vector map has been proposed to address the problem caused by uneven aerosol distribution w.r.t. altitude variation. A Random Decision Forest model has been adopted to deal with the additional light attenuation and transmission map underestimation problem caused by heavy haze. Experiment shows the proposed model produces much better visual restoration for heavy haze weathers compared to state-of-the-art methods in terms of colour fidelity, noise reduction, and overall contrast.
AB - Extreme weather hazards happens more often these days due to climate changes and increased human industrial activities, and one of most notorious of them is haze. State-of-the-art haze removal methods generally work well with light haze conditions, however when haze gets heavier, the physical model tend to produce over-shadowed, noisy, and color distorted restorations. A new physical model has been proposed in this paper for heavy haze weathers. An airlight vector map has been proposed to address the problem caused by uneven aerosol distribution w.r.t. altitude variation. A Random Decision Forest model has been adopted to deal with the additional light attenuation and transmission map underestimation problem caused by heavy haze. Experiment shows the proposed model produces much better visual restoration for heavy haze weathers compared to state-of-the-art methods in terms of colour fidelity, noise reduction, and overall contrast.
KW - dark channel prior
KW - extremely heavy haze
KW - random decision forest
UR - http://www.scopus.com/inward/record.url?scp=84946198601&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2015.7168952
DO - 10.1109/ISCAS.2015.7168952
M3 - Conference article published in proceeding or book
AN - SCOPUS:84946198601
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 1590
EP - 1593
BT - 2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Symposium on Circuits and Systems, ISCAS 2015
Y2 - 24 May 2015 through 27 May 2015
ER -
