利用多帧图像分布特性的图像重构算法研究

研究图像恢复高分辨率图像的问题。由于图像存在锐化和冗余信息而影响了矢量效果,针对以往用反卷积或者单幅图像进行图像恢复重构方法结果不够理想的缺陷,提出了一种利用多幅图像进行图像重构的方法。算法核心思想是利用多幅相关图像进行训练,从而得到某些观测的分布特性,从而对图像进行恢复。首先,定义了一个基于图像小块的马尔科夫随机场分布模型,并由此定义了隐含节点到观测之间的势能函数和隐含节点之间的势能函数;然后通过选用一组训练图像,计算隐含节点到观测之间的混合高斯分布模型,并利用相邻节点所对应的小块之间相邻边界的相关度来计算势能函数所对应的值。最后利用置信传播的方法计算整幅图像上最优的恢复结果。仿真结果显示提出的方法较传统的反卷积方法和插值方法具有更优的恢复结果,能很好的逼近原始的真实图像。

EFFECTIVE IMAGE SEGMENTATION FRAMEWORK FOR GAUSSIAN MIXTURE MODEL INCORPORATING LOCAL INFORMATION

A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.

An Aircraft Trajectory Anomaly Detection Method Based on Deep Mixture Density Network

The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories.Low anomaly detection accuracy still exists due to the high-dimensionality,heterogeneity and temporality of flight trajectory data.To this end,this paper proposes an abnormal trajectory detection method based on the deep mixture density network(DMDN)to detect flights with unusual data patterns and evaluate flight trajectory safety.The technique consists of two components:Utilization of the deep long short-term memory(LSTM)network to encode features of flight trajectories effectively,and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model(GMM).Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories.The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods.The proposed model can be used as an assistant decision-making tool for air traffic controllers.

Improved dark channel image dehazing method based on Gaussian mixture model

To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky,an improved dark channel image dehazing method based on Gaussian mixture model is proposed.Firstly,we use the Gaussian mixture model to model the hazy image,and then use the expectation maximization(EM)algorithm to optimize the parameters,so that the hazy image can be divided into the sky region and the non-sky region.Secondly,the sky region is divided into a light haze region,a medium haze region and a heavy haze region according to the different dark channel values to estimate the transmission respectively.Thirdly,the restored image is obtained by combining the atmospheric scattering model.Finally,adaptive local tone mapping for high dynamic range images is used to adjust the brightness of the restored image.The experimental results show that the proposed method can effectively eliminate the color distortion in the sky region,and the restored image is clearer and has better visual effect.

Online split-and-merge expec tation-maximization training of Gaussian mixture model and its optimization

This paper presents a new online incremental training algorithm of Gaussian mixture model （GMM）, which aims to perform the expectation-maximization（EM） training incrementally to update GMM model parameters online sample by sample, instead of waiting for a block of data with the sufficient size to start training as in the traditional EM procedure. The proposed method is extended from the split-and-merge EM procedure, so inherently it is also capable escaping from local maxima and reducing the chances of singularities. In the application domain, the algorithm is optimized in the context of speech processing applications. Experiments on the synthetic data show the advantage and efficiency of the new method and the results in a speech processing task also confirm the improvement of system performance.