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In this paper, we propose a deep generative adversarial network forsuper-resolution considering the trade-off between perception and distortion.Based on good performance of a recently developed model for super-resolution,i.e., deep residual network using enhanced upscale modules (EUSR), the proposedmodel is trained to improve perceptual performance with only slight increase ofdistortion. For this purpose, together with the conventional content loss,i.e., reconstruction loss such as L1 or L2, we consider additional losses inthe training phase, which are the discrete cosine transform coefficients lossand differential content loss. These consider perceptual part in the contentloss, i.e., consideration of proper high frequency components is helpful forthe trade-off problem in super-resolution. The experimental results show thatour proposed model has good performance for both perception and distortion, andis effective in perceptual super-resolution applications.

Generative adversarial network-based image super-resolution using perceptual content losses