基于卷积神经网络的视频图像超分辨率重建方法

为了进一步增强视频图像超分辨率重建的效果，研究利用卷积神经网络的特性进行视频图像的空间分辨率重建，提出了一种基于卷积神经网络的视频图像重建模型。采取预训练的策略用于重建模型参数的初始化，同时在多帧视频图像的空间和时间维度上进行训练，提取描述主要运动信息的特征进行学习，充分利用视频帧间图像的信息互补进行中间帧的重建。针对帧间图像的运动模糊，采用自适应运动补偿加以处理，对通道进行优化输出得到高分辨率的重建图像。实验表明，重建视频图像在平均客观评价指标上均有较大提升（PSNR +0.4 dB / SSIM +0.02），并且有效减少了图像在主观视觉效果上的边缘模糊现象。与其他传统算法相比，在图像评价的客观指标和主观视觉效果上均有明显的提升，为视频图像的超分辨率重建提供了一种基于卷积神经网络的新颖架构，也为进一步探索基于深度学习的视频图像超分辨率重建方法提供了思路。

Video image super-resolution reconstruction method based on convolutional neural network

In order to further improve the performance of video image super-resolution reconstruction and study the reconstruction of spatial resolution of video images by using the characteristics of convolution neural network, this paper proposed a video image reconstruction model based on convolution neural network. The model adopted the pre-training strategy to initialize the parameters. And it carried out the training processing both on the spatial and temporal dimensions of the multi-frame video images at the same time. It extracted the characteristics of the main motion information, learn and make full use of the information inter the frames for improved performance. And it used the adaptive motion compensation algorithm to optimize the output of the channel to obtain the reconstructed center frame image with high resolution. The experimental results show that the average of objective evaluation indexes for video image reconstruction has improved with a rather clear margin (PSNR + 0.4 dB / SSIM + 0.02) , and the edge of the fuzzy phenomenon in video reconstruction image for the subjective visual effect has been effectively reduced. Compared with other traditional algorithms, the evaluation of the objective indexes and subjective visual effect of the reconstructed image are both obviously improved. Provide a novel architecture based on convolution neural network for video image super-resolution, which provides an exploration for the further study of video image super-resolution reconstruction based on the deep learning method.