ing at the problems of blurred edge details and loss of image features in the process of image super-resolution reconstruction, a super-resolution reconstruction algorithm based on dense connection generative adversarial network is proposed. This algorithm consists of a generative network and a discriminative network. In the generative network structure, the original low-resolution image is used as the input of the network. In order to make full use of the features, the features of the shallow network are transferred to each layer of the deep network structure using dense connection, so as to effectively avoid the loss of image features. Sub-pixel convolution is performed at the end, and the image is deconvolved to complete the final super-resolution reconstruction of the image, which greatly reduces the training time. In the discriminative network structure, 6 convolutional modules and a fully connected layer are used to identify true and false images, and the idea of adversarial games is used to improve the quality of reconstructed images. Experimental results show that the proposed algorithm has greatly improved the visual effect assessment, peak signal to noise ratio value, structural similarity value, time-consuming, and indicators. It has restored richer image detail information and achieved better visual effects and comprehensive characteristic.

ing at the problem of low recognition accuracy of small faces in classroom scene, this paper proposes a lightweight network structure (Dual-MobileFaceNet) combining channel addition and channel concatenation based on the InsightFace algorithm by integrating the MobileFaceNet and DenseNet structures, so as to improve the recognition speed and the recognition accuracy of small faces. Meanwhile, a double classification algorithm is proposed to improve the identification and classification ability of the InsightFace algorithm. The proposed algorithm achieves an accuracy of 99.46% on LFW dataset. Finally, the proposed algorithm is transplanted to Jetson TX2 embedded development board. In 8- and 18-people classrooms, the recognition accuracy of the proposed algorithm is 96.24% and 94.68%, and the recognition speed of each frame is 0.14 s and 0.29 s, respectively. Compared with other large networks, the proposed network is more realistic and efficient. The proposed algorithm provides an effective concept for the classroom face recognition and non perception attendance system.