Research on Face Super Resolution Reconstruction Based on DBGAN

Abstract

Super-resolution reconstruction technology is an important research topic in many fields such as image processing and computer vision. This technology can be used widely for security monitoring, old image reconstruction, image compression transmission and other fields. In this paper, super-resolution image reconstruction is performed on a low-resolution image of four times magnification. We propose the dense convolutional networks used as a generator instead of residual networks, and set perceptual loss as the optimization goal. We use the VGG network feature map as the loss function instead of Mean Square Error, which combines the perceptual loss with the adversarial loss and is beneficial for compensating the shortcomings of previous methods that lack high frequency detail. Experimental results show that our method can produce clearer face images than the traditional methods. These reconstructed images have higher resolution and Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) than the images generated by the deep residual networks.