An efficient haze removal algorithm using chromatic properties
Yao Wang, Fangfa Fu, Weizhe Xu, Jinjin Shi, Jinxiang Wang · 2017
Fog degrades the quality of road images causing errors in stereo matching and road segmentation for Advanced Driver Assistance Systems (ADAS). Accident rates can be reduced if robust and efficient algorithms are applied for road image fog removal. Many studies have been conducted on this subject to date, but existing methods are not optimized for road images. Several daytime models cause local darkness, and blurring artifacts result in low quality haze-free images, and nighttime models are altogether inefficient. This study focuses on dehazing daytime and nighttime images by utilizing chromatic properties to remove haze from images. The proposed method treats fog as a specular pixels of dual consistency and physical properties, and the dehazing reflection model is suitable for parallel implementation to efficiently detect fog pixels. An edge-preserving low-pass filter (a fast-bilateral filter running 230x faster than an average CPU) is used to smooth the color components' original image maximum fraction to remove noise among fog pixels. The method significantly outperforms existing baselines in regards to both efficiency and dehazing.