Open AccessHaze removal for unmanned aerial vehicle aerial video based on spatial‐temporal coherence optimisation
Author(s) -
Zhao Xintao,
Ding Wenrui,
Liu Chunhui,
Li Hongguang
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0060
Subject(s) - computer science , coherence (philosophical gambling strategy) , haze , computer vision , artificial intelligence , frame (networking) , spatial coherence , distortion (music) , coherence time , computational complexity theory , spatial filter , consistency (knowledge bases) , remote sensing , algorithm , geology , geography , mathematics , telecommunications , statistics , meteorology , amplifier , bandwidth (computing)
Haze removal is a non‐trivial work for unmanned aerial vehicle (UAV) aerial video processing, and challenges are mainly attributed to spatial‐temporal coherence and computational efficiency. The authors propose a novel dehazing algorithm for hazy UAV aerial video and improve the classical dark channel prior approach with a bright region filling process to alleviate colour distortion in the recovered video, which enhances the spatial consistency. To achieve better temporal coherence, the authors constrain the atmospheric light estimation between adjacent frames by using a temporal filter. The authors also optimise the transmission calculation to reduce computational complexity. Experimental findings show that the proposed algorithm yields results superior to those obtained from previous methods. Compared with that in frame‐by‐frame dehazing, the processing time in the proposed method is reduced by 74.5%.