Super-resolved image perceptual quality improvement via multifeature discriminators

Abstract. Generative adversarial network (GAN) for image super-resolution (SR) has attracted enormous interests in recent years. However, the GAN-based SR methods only use image discriminator to distinguish SR images and high-resolution (HR) images. Image discriminator fails to discriminate images accurately since image features cannot be fully expressed. We design a GAN-based SR framework GAN-IMC, which includes generator, image discriminator, morphological component discriminator, and color discriminator. The combination of multiple feature discriminators improves the accuracy of image discrimination. Adversarial training between the generator and multifeature discriminators forces SR images to converge with HR images in terms of data and features distribution. Moreover, in some cases, feature enhancement of feature-rich region is also worth considering. GAN-IMC is further optimized by weighted content loss (GAN-IMCW), which effectively restores and enhances feature-rich regions in SR images. The effectiveness and robustness of the proposed method are confirmed by extensive experiments on public datasets. Compared with state-of-the-art methods, the proposed method not only achieves competitive perceptual index and natural image quality evaluator values but also obtains pleasant visual perception in edge, texture, color, and feature-rich regions.