关键词: 注意力机制, 稠密连接, 人脸超分, 特征融合, 神经网络

Abstract:

Face super-resolution reconstruction is a low-cost technique for obtaining low-resolution face processing to obtain high-resolution face, also known as face illusion. In order to make the reconstructed face image have clearer detail texture, this paper proposes a densely connected face super-resolution network based on attention mechanism by studying the face-based super-division algorithm based on deep learning. The algorithm consists of two parts：feature extraction and image  reconstruction. By focusing on the information of the feature channel domain and the spatial domain, the Multi Attention Domain Module（MADM） is proposed. Among them, by changing the mutual relationship and weight of the channel and space, the features are weighted and reorganized adaptively, and the dense layered and long and short connections are used to fuse the features of different layers to improve network performance. The experimental results verify the correctness of the proposed algorithm. Compared with the existing algorithms, the superior performance of the proposed algorithm is demonstrated. The reconstructed face image has clearer texture details.

Key words: attention mechanism, dense connection, face super-resolution, feature fusion, neural network