Perceptual Quality Preserving Image Super-resolution via Channel Attention

Generative Adversarial Network (GAN) has been widely applied on Single Image Super-Resolution (SISR) problems. However, there can be quite a variability in the results from the GAN-based methods. In some cases, the GAN-based methods might cause structure distortion, which can be easily distinguished by human beings, especially for artificial structures, because the methods only focus on the perceptual quality of the whole image. On the other hand, PSNR-oriented methods can prevent structure distortion but with overly smoothed context. To overcome these problems, we propose a deep neural net refiner for SISR methods, not only improving perceptual quality but also preserving context structures. In the experiments, our model qualitatively and quantitatively performs favorably against the state-of-the-art SISR methods.