Author(s)

Pengyan Xie, Yanxiong Niu, Haisha Niu, Dan Guo

Corresponding Author

Yanxiong Niu

ABSTRACT

Deep convolutional neural networks (Deep CNNs) have recently demonstrated high-quality reconstruction for face image super-resolution. However, as the depth grows, more computations are required and it is difficult to train the network. In this paper, a highly efficient and faster face image super-resolution method using a piecewise-recursive convolution network (PRCN) is proposed. Original low-resolution (LR) images are used as the inputs of the proposed model and thus significantly reduce the calculation cost. A combination of recursive convolutional networks and skip connection layers are used to extract both local and global features of input LR face images. Specially, the number of each recursive convolutional layer is optimized to further improve the performance and reduce the computation. For image reconstruction, 1×1 convolutional layers are used to reduce the dimension of the extracted features. Parallelized CNNs are then applied to learn an effective nonlinear mapping from the low-resolution (LR) to the high-resolution (HR) features. Experimental results show that the proposed algorithm outperforms the state-of-the-art methods, while achieving faster and more efficient computation.

KEYWORDS

Face Images, Super-Resolution, Deep Cnns, Recursive Convolutional Networks, Skip Connection, Network