Abstract
Deep learning is a hot method in face super-resolution reconstruction in recent years, but it needs to further improve the details of reconstructed images and speed up network training. This paper improves the deep residual network from two aspects of residual unit and network structure and proposes a face super-resolution reconstruction algorithm based on the improved model. We improve the network structure of the residual unit by connecting with a densely connected convolutional layer and removing the BN layer, thereby enhancing the information flow between the inner convolutional layers and eliminate the damage to the spatial information of the image by batch normalization processing. At the same time, we combine the output characteristics of each residual unit on the basis of the global residual structure, so the face feature information is more fully utilized and the model detail recovery ability is also improved. Experiments on FDDB and AFLW face datasets show that the proposed method has better performance in feature description and detail information reconstruction, and higher PSNR and SSIM than other methods.
Highlights
Face recognition applications continue to heat up, and face-related research has attracted wider attention than before
The first one is, learning rate is fixed to 0.0001, and SGD algorithm is used to optimize the gradient; the second one is initial learning rate is 0.0001 and the learning rate reduces by 0.1 times every 20 epochs, at the same time, SGD algorithm is used ; the third one is, initial learning rate is 0.0001, using Adam algorithm is adopted for gradient optimization, which can adjust the learning rate adaptively according to the network training situation
5 Conclusions In view of the problems of insufficient detail recovery and slow network optimization existing in face superresolution reconstruction, this paper improves the deep residual network from the residual unit and network
Summary
Face recognition applications continue to heat up, and face-related research has attracted wider attention than before. Face super-resolution reconstruction plays an important role in face recognition in the natural environment. Image super-resolution (SR) reconstruction mainly includes interpolation-based methods [1–3], reconstruction-based methods [4–6], and learning-based methods [7–13]. The method based on interpolation and reconstruction is simple to implement and the computation cost is small, but the reconstruction effect is poor when the scale is large, and it cannot deal with complex image structure [7]. With the rapid development of deep learning, convolutional neural network (CNN) [13] has been applied to image super-resolution reconstruction. In 2014, Dong et al firstly proposed super-resolution CNN (SRCNN) based on convolution neural network, which utilized the strong feature expression ability of convolution neural network to improve the accuracy of reconstructed image [14].
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