  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. In this paper, we design a new 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 multi-feature discriminators
forces SR images to converge with HR images in terms of data and features
distribution. Moreover, in some cases, feature enhancement of salient regions
is also worth considering. GAN-IMC is further optimized by weighted content
loss (GAN-IMCW), which effectively restores and enhances salient regions in SR
images. The effectiveness and robustness of our 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
(PI) and Natural Image Quality Evaluator (NIQE) values but also obtains
pleasant visual perception in image edge, texture, color and salient regions.
