Learning deep transmission network for single image dehazing
State of the art single image dehazing algorithms have some challenges to deal with images captured under complex weather conditions because their assumptions usually do not hold in those situations. In this paper, we develop a deep transmission network for robust single image dehazing. This deep transmission network simultaneously copes with three color channels and local patch information to automatically explore and exploit haze relevant features in a learning framework. We further explore different network structures and parameter settings to achieve tradeoffs between performance and speed, which shows that color channels information is the most useful haze relevant feature rather than local information. Experiment results demonstrate that the proposed algorithm outperforms state of the art methods on both synthetic and real world datasets.
2016 IEEE International Conference on Image Processing (ICIP)