Time Series Modeling for an Adaptive Visibility Improvement of the Outdoor Image Sequences in Wavelet Domain
Under a complex atmosphere, the visibility of images captured by a moving camera needs to be enhanced so as to overcome various atmospheric perturbations. To achieve a stable and robust performance, in this paper, we propose to build a time series based model in wavelet domain by employing both spatial and temporal information of the sequential images. First, we set up a blind evaluation criteria of the Image Quality (IQ). Second, to each frame of the sequence, we utilize an adaptive computation framework to improve the performance of the wavelet based algorithm, which is controlled by the criteria above. After that, we build two time series models separately, i.e., an Autoregressive Integrated Moving Average (ARIMA) model and an Autoregressive Conditional Heteroscedasticity (ARCH) model to fit the parameters change of the complex atmosphere. Finally, we utilize these two models to guide and improve the adjustment of the adaptive calculation. Experiment results demonstrate the effectiveness and robustness of our solution in many outdoor applications.
Haoting LIU Hanqing LU Fenggang XU
Laboratory of System Usability Evaluation,Astronaut Research & Training Center of China,Beijing,Chin National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beiji Astronaut Research & Training Center of China,Beijing,China
国际会议
2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)
桂林
英文
1247-1252
2009-12-19（万方平台首次上网日期，不代表论文的发表时间）