  The proposal of perceptual loss solves the problem that per-pixel difference
loss function causes the reconstructed image to be overly-smooth, which
acquires a significant progress in the field of single image super-resolution
reconstruction. Furthermore, the generative adversarial networks (GAN) is
applied to the super-resolution field, which effectively improves the visual
quality of the reconstructed image. However, under the condtion of high
upscaling factors, the excessive abnormal reasoning of the network produces
some distorted structures, so that there is a certain deviation between the
reconstructed image and the ground-truth image. In order to fundamentally
improve the quality of reconstructed images, this paper proposes a effective
method called Dual Perceptual Loss (DP Loss), which is used to replace the
original perceptual loss to solve the problem of single image super-resolution
reconstruction. Due to the complementary property between the VGG features and
the ResNet features, the proposed DP Loss considers the advantages of learning
two features simultaneously, which significantly improves the reconstruction
effect of images. The qualitative and quantitative analysis on benchmark
datasets demonstrates the superiority of our proposed method over
state-of-the-art super-resolution methods.
