Research of a video super-resolution reconstruction model based on attention and reverseattention mechanism
Aiming at the problems of noise amplification and feature loss in the process of video super-resolution reconstruction,on the basis of BasicVSR++model,a propagation model based on attention and reverse attention mechanism is proposed to optimize the video super-resolution reconstruction.The model decomposes the original features into propagated features and redundant features.The propagation features spread the information in the propagation network,while the redundancy features are extracted in depth at the residual network,and finally,the PixelShuffle network fuses and reconstructs the acquired two parts of the features to achieve better super-resolution reconstruction results.In the published dataset REDS,PNSR(Peak Signal-to-Noise Ratio)reaches 32.48 dB,the performance of video super-resolution reconstruction is improved.
video super-resolution reconstructionattention and reverse attention mechanismpropagation network
国家科技期刊平台
NSTL
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