Adaptive modulation attention-based face super-resolution reconstruction method

Abstract

In view of the large deviation of the pixel value of the generated image caused by the abandonment of the BN layer by the previous deep face super-resolution module, and the inaccuracy of the face prior alignment module, an adaptive modulation super-resolution neural network combining the attention mechanism and face alignment is proposed. Firstly, in order to solve the problem of inaccurate face alignment, a specific attention module is used to extract features with low resolution, and the output feature map is aligned with the key point feature map to increase the accuracy of positioning landmarks. Secondly, aiming at the problem that local pixels have maximum values, an adaptive modulation super-division module is proposed to make the reconstructed image more suitable for visual senses. The experimental results show that compared with face super-resolution algorithms such as end-to-end learning facial prior network (FSRNET), facial landmark attention network (PFSR) and deep iterative collaboration network (DIC), better visual effects and performance indicators are achieved.