Advanced Generative Adversarial Network for Image Superresolution

Abstract

AbstractThe superresolution (SR) method based on generative adversarial networks (GANs) cannot adequately capture enough diversity from training data, resulting in misalignment between input low resolution (LR) images and output high resolution (HR) images. GAN training has difficulty converging. Based on this, an advanced GAN-based image SR reconstruction method is presented. First, the dense connection residual block and attention mechanism are integrated into the GAN generator to improve high-frequency feature extraction. Meanwhile, an added discriminator is added into the GAN discriminant network, which forms a dual discriminator to ensure that the process of training is stable. Second, the more robust Charbonnier loss is used instead of the mean square error (MSE) loss to compare similarities between the obtained image and actual image, and the total variation (TV) loss is employed to smooth the training results. Finally, the experimental results indicate that global structures can be better reconstructed using the method of this paper and texture details of images compared with other SOTA methods. The peak signal-to-noise ratio (PSNR) values by the method of this paper are improved by an average of 2.24 dB, and the structural similarity index measure (SSIM) values are improved by an average of 0.07.KeywordsGenerative adversarial networks (GANs)Superresolution (SR)Residual dense blockAttention mechanismDual discriminator