关键词: 卷积神经网络（CNN）, 超分辨率图像重建, 生成式对抗网络（GAN）, 四倍采样

Abstract:

Aiming at the problems of over-fitting of existing convolutional neural network image super-resolution algorithm and insufficient convergence of loss function, combined with super-resolution algorithm and generative adversarial network (GAN) theory, the super-resolution algorithm PESRGAN (permeability enhanced super-resolution generative adversarial networks) is used to recover quadruple downsampled images. Firstly, the residual dense block (RDB) is used as the basic structural unit, which effectively avoids the over-fitting problem. Secondly, the double-layer feature loss is used and the permeability index (PI) is used as the weight of the loss, so that it can better learn the mapping relationship between low-resolution and high-resolution images; VGG19 is used as the discriminant network high-resolution image for classification. Finally, PESRGAN algorithm is compared with Bicubic, SRGAN(super-resolution using a generative adversarial network) and ESRGAN (enhanced super-resolution generative adver-sarial networks) algorithm in objective parameters and subjective visual effects using classical data sets. The experi-mental results show that the average peak signal to noise ratio (PSNR) of PESRGAN is 25.4 dB, the average stru-ctural similarity index (SSIM) is 0.73 and the average PI is 1.15 on the classical data sets. The objective parameters and subjective evaluation of PESRGAN are superior to other algorithms, which proves that PESRGAN has good super-resolution reconstruction effect.

Key words: convolutional neural network (CNN), super-resolution image reconstruction, generative adversarial network (GAN), quadruple sampling