Abstract
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.
Highlights
Face images provide important information for human visual perception and computer analysis, which have been widely studied in recent years
Super-resolution reconstruction is an effective method to improve the resolution of face images, which is of great significance to improve the image quality and enhance the richness of face information
In 2014, Dong et al [17] firstly proposed super-resolution reconstruction based on convolutional neural network (SRCNN), which utilized the strong feature expression ability of convolutional neural network to improve the accuracy of the reconstructed image
Summary
Face images provide important information for human visual perception and computer analysis, which have been widely studied in recent years. In 2014, Dong et al [17] firstly proposed super-resolution reconstruction based on convolutional neural network (SRCNN), which utilized the strong feature expression ability of convolutional neural network to improve the accuracy of the reconstructed image. Due to the few convolutional layers, small receptive field and poor generalization ability, the network is difficult to extract the deep features of the image, and the reconstruction performance is limited. In 2015, Zhou et al [21] proposed a bi-channel convolutional neural network to improve the problem of the face feature information loss to some extent by cross-layer output of input images. In 2016, Zhu et al [23] proposed a two-stage iterative method for face super-resolution reconstruction, which is difficult to train and had no obvious improvement effect. The network can learn purposefully, which is more conducive to the accurate reconstruction of face details, and the reconstruction effect is significantly improved
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