Face Super-Resolution Reconstruction Based on Self-Attention Residual Network

Aiming at the problems of the face image super-resolution reconstruction method based on convolutional neural network, such as single feature extraction scale, low utilization rate of features and blurred face images texture, a model combining convolutional neural network with self-attention mechanism is proposed. Firstly, the shallow features of the image are extracted by the cascaded 3 × 3 convolutional kernels, and then self-attention mechanism is combined with the residual blocks in depth residual network to extract the deep detail features of faces. Finally, the extracted features are fused globally by skip connections, which provide more high-frequency details for face reconstruction. Experiments on Helen, CelebA face datasets and real-world images showed that the proposed method could make full use of facial feature information, and its peak signal to noise ratio (PSNR) and structural similarity (SSIM) were both higher than the comparison methods with better subjective visual effects.