  Traditional dehazing techniques, as a well studied topic in image processing,
are now widely used to eliminate the haze effects from individual images.
However, even the state-of-the-art dehazing algorithms may not provide
sufficient support to video analytics, as a crucial pre-processing step for
video-based decision making systems (e.g., robot navigation), due to the
limitations of these algorithms on poor result coherence and low processing
efficiency. This paper presents a new framework, particularly designed for
video dehazing, to output coherent results in real time, with two novel
techniques. Firstly, we decompose the dehazing algorithms into three generic
components, namely transmission map estimator, atmospheric light estimator and
haze-free image generator. They can be simultaneously processed by multiple
threads in the distributed system, such that the processing efficiency is
optimized by automatic CPU resource allocation based on the workloads.
Secondly, a cross-frame normalization scheme is proposed to enhance the
coherence among consecutive frames, by sharing the parameters of atmospheric
light from consecutive frames in the distributed computation platform. The
combination of these techniques enables our framework to generate highly
consistent and accurate dehazing results in real-time, by using only 3 PCs
connected by Ethernet.
