图-谱结合的压缩感知高光谱视频图像复原

In this paper, a new graph-spectral hyperspectral video restoration method regarding the imaging characteristics of dynamic scenes recorded by liquid crystal tunable filter hyperspectral imaging system is proposed. Firstly, the hyperspectral image of the moving foreground target is obtained by the foreground target detection, and the moving foreground target is separated from the background region. Then the background region is divided into the motion region which is obscured by the foreground target and the still region which is not obscured by the foreground target according to the foreground target detection result. Based on the correlation of the spatial dimension and spectral dimension of the hyperspectral image, dictionary learning is performed on the still region to obtain sparse prior information. Combined with compressed sensing theory for motion region recovery, a complete background region hyperspectral image is obtained. Finally, the moving foreground target hyperspectral image is combined with the background region hyperspectral image to obtain a hyperspectral video image. The experimental results show that the proposed method of hyperspectral video image restoration outperforms the existing algorithm in terms of peak signal-to-noise ratio and visual effect, and the peak signal-to-noise ratio is increased by an average of more than 5 dB.