Component-Based Distributed Framework for Coherent and Real-Time Video Dehazing
Meihua Wang, Jiaming Mai, Yun Liang, Tom Z. J. Fu, Zhenjie Zhang,, Ruichu Cai

TL;DR
This paper introduces a distributed, real-time video dehazing framework that enhances coherence and efficiency by decomposing algorithms into components and sharing parameters across frames, suitable for video analytics.
Contribution
It proposes a novel distributed framework with component decomposition and cross-frame normalization, enabling real-time, coherent video dehazing on limited hardware.
Findings
Achieves real-time dehazing on 3 PCs connected by Ethernet.
Enhances frame-to-frame coherence through parameter sharing.
Optimizes processing efficiency via automatic CPU resource allocation.
Abstract
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…
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Taxonomy
TopicsImage Enhancement Techniques · Video Surveillance and Tracking Methods · Advanced Vision and Imaging
