DAW-GAN: a generative adversarial network based on the dynamic adaptive weight for image super-resolution

Image super-resolution (SR) is a critical task in computer vision and image processing, with a wide range of real-life applications. Its goal is to reconstruct high-resolution (HR) images from low-resolution (LR) images. In recent years, deep neural networks have made significant advancements in this field. However, existing super-resolution algorithms have a large number of parameters and the reconstructed image details are too smooth and blurred. To address this problem, we propose a dynamic adaptive weight-based generative adversarial network (DAW-GAN). Our network introduces a dynamic attention module (DAM), in which the weights of attention and non-attention branches can be adaptively adjusted by a dynamic weight module (DWM). Additionally, we supplement the network with a "distance" information loss function to optimize the training of the discriminator, in addition to perceptual loss. Our experimental results demonstrate that the reconstructed images from our network exhibit finer texture details and are more consistent with human visual perception.