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The solution of a high-resolution (HR) image corresponding to a low-resolution (LR) image is not unique in most cases. However, single-LR-single-HR supervision is widely adopted in single-image super-resolution (SISR) tasks, which leads to inflexible inference logic of the model and poor generalization ability. To improve the flexibility of model inference, we constructed a novel form of supervision, except for the ground truth (GT). Specifically, considering the structural properties of natural images, we propose using extra supervision to focus on the textural similarity of the images. As textural similarity does not account for the position information of images, a Gram matrix was constructed to break the limitations of spatial position and focus on the textural information. Besides the use of traditional perceptual loss, we propose a discriminator perceptual loss based on the two-network architecture of generative adversarial networks (GAN). The difference between the discriminator features used in this loss and the traditional visual geometry group (VGG) features is that the discriminator features can describe the relevant information from the perspective of super-resolution. Quantitative and qualitative experiments were performed to demonstrate the effectiveness of the proposed method.

Gram-GAN: Image Super-Resolution Based on Gram Matrix and Discriminator Perceptual Loss