结合注意力机制与多尺度特征融合的视频彩色化方法.

To address the issue that existing video colorization methods are complicated to guarantee both coloring quality and temporal consistency, this paper proposed a video colorization method AMVC-GAN combining attention mechanism and multi-scale feature fusion. Firstly, it proposed a GAN-based video colorization network model. It designed a multi-scale feature fusion module in the generator of GAN with a cyclic time network as the main body to obtain information of different time frequencies. Secondly, to effectively consider the relationship between adjacent frames, it used the features extracted from different time frequencies to strengthen the connection between frames as a way to enhance the temporal consistency of colorization. Finally, to obtain more helpful information, it introduced an attention module in the upsampling part, and optimally trained the results by utilizing PatchGAN to enhance the final colorization effect. Comparing with the state-of-the-art automatic video colorization methods on DAVIS and VIDEVO datasets, the results show that AMVC-GAN ranks first in multiple indicators, with better time consistency and colorization effect. Compared with other methods, AMVC-GAN can effectively reduce time flicker, while ensuring more real and natural colorization effect. [ABSTRACT FROM AUTHOR]