Concerning the recovery problem of high-frequency information like texture details in image super-resolution reconstruction, an image super-resolution reconstruction method based on self-attention network was proposed. Two reconstruction stages were used to gradually restore the image accuracy from-coarse-to-fine. In the first stage, firstly, a Low-Resolution (LR) image was taken as the input through a Convolutional Neural Network (CNN), and a High-Resolution (HR) image was output with coarse precision; then, the coarse HR image was used as the input and a finer HR image was produced. In the second stage, the correlation of all positions between features was calculate by the self-attention module, and the global dependencies of features were captured to enhance texture details. Experimental results on the benchmark datasets show that, compared with the state-of-the-art deep neural networks based super-resolution algorithms, the proposed algorithm not only has the best visual effect, but also has the Peak Signal-to-Noise Ratio (PSNR) improved averagely by 0.1dB and 0.15dB on Set5 and BDSD100. It indicates that the network can enhance the global representation ability of features to reconstruct high quality images.

To improve the capacity and invisibility of image steganography, the article analyzed the advantage and application fields between Nonsubsampled Contourlet Transform (NSCT) and Contourlet transform. Afterwards, an image steganography was put forward, which was based on Human Visual System (HVS) and NSCT. Through modeling the human visual masking effect, different secret massages were inserted to different coefficient separately in the high-frequency subband of NSCT. The experimental results show that, in comparison with the steganography of wavelet, the proposed algorithm can improve the capacity of steganography at least 70000b，and Peak Signal-to-Noise Ratio (PSNR) increases about 4dB. Therefore, the invisibility and embedding capacity are both considered preferably, which has a better application outlook than the wavelet project.