DANet: Dual-domain Alignment Network for Face Super-Resolution

Limited by poor imaging conditions, the collected facial images often exhibit inferior visual quality, which affects downstream tasks such as face recognition and attribute analysis. The existing face super-resolution methods lack effective information acquisition and alignment mechanisms, leading to information loss. To solve the issues, this paper proposes a dual-domain alignment network, which extracts multi-scale and multi-level local and global facial features through an adaptive residual module and a complex convolution residual module. By setting adaptive parameters, the residual module can adaptively adjust the fusion coefficients according to input information, enabling the network to possess better feature representation capability. In the complex convolution residual module, a complex convolution block is introduced to realize joint estimation of frequency-domain information. Furthermore, a lightweight alignment and enhancement module is designed to align the features of dual-branch and enhance spatial domain features. Experimental results demonstrate that the proposed method can reconstruct more natural face images with fewer parameters, and outperforms existing face super-resolution methods.