关键词: 人脸超分辨率重建, 生成对抗网络, Wasserstein距离, Wasserstein散度

Abstract:

Due to the influence of illumination and imaging equipment and other factors, the single frame of face image collected has a low resolution, which makes it impossible to perform accurate face recognition. Therefore, image super-resolution reconstruction is required. However, in the process of face super-resolution reconstruction using SRGAN model, gradient disappearance or explosion is easy to occur, which seriously affects the accuracy and quality of the reconstructed image. According to the above problem, this paper puts forward the improvement based on the generated against network face super-resolution reconstruction algorithm, on the basis of SRGAN combination of WGA-N introduction out divergence, and to maximize its [T] get optimal scalar functions, implementation constraints can remove Lipschit-z Wassertein distance can be obtained directly, and generation network of the objective function is obtained by minimizing Wassertein distance, finally the improved model can improve the quality of reconstruction image. The experimental results show that this method can generate high-resolution face images and is better than DRCN, FSRCNN, SRGAN_WGAN, VDSR and DRRN models in both subjective and objective evaluation indexes.

Key words: face super-resolution, generative adversarial network, Wasserstein distance, Wasserstein divergence