Face image super-resolution reconstruction algorithm based on residual attention mechanism

Aiming at the problems such as low reconstruction efficiency, fuzzy texture details, and difficult convergence of reconstruction network face image super-resolution reconstruction algorithms, a new super-resolution reconstruction algorithm with residual concern was proposed. Firstly, to solve the influence of redundant and invalid information about the face image super-resolution reconstruction network, an attention mechanism was introduced into the feature extraction module of the network, which improved the feature utilization rate of the overall network. Secondly, to alleviate the problem of gradient disappearance, the adaptive residual was introduced into the network to make the network model easier to converge during training, and features were supplemented according to the needs during training. The experimental results showed that the proposed algorithm had better reconstruction performance, more facial details, and clearer texture in the reconstructed face image than the comparison algorithm. In objective evaluation, the proposed algorithm's peak signal-to-noise ratio and structural similarity were also better than other algorithms.