  The presence of residual and dense neural networks which greatly promotes the
development of image Super-Resolution(SR) have witnessed a lot of impressive
results. Depending on our observation, although more layers and connections
could always improve performance, the increase of model parameters is not
conducive to launch application of SR algorithms. Furthermore, algorithms
supervised by L1/L2 loss can achieve considerable performance on traditional
metrics such as PSNR and SSIM, yet resulting in blurry and over-smoothed
outputs without sufficient high-frequency details, namely low perceptual
index(PI). Regarding the issues, this paper develops a perception-oriented
single image SR algorithm via dual relativistic average generative adversarial
networks. In the generator part, a novel residual channel attention block is
proposed to recalibrate significance of specific channels, further increasing
feature expression capabilities. Parameters of convolutional layers within each
block are shared to expand receptive fields while maintain the amount of
tunable parameters unchanged. The feature maps are subsampled using sub-pixel
convolution to obtain reconstructed high-resolution images. The discriminator
part consists of two relativistic average discriminators that work in pixel
domain and feature domain, respectively, fully exploiting the prior that half
of data in a mini-batch are fake. Different weighted combinations of perceptual
loss and adversarial loss are utilized to supervise the generator to
equilibrate perceptual quality and objective results. Experimental results and
ablation studies show that our proposed algorithm can rival state-of-the-art SR
algorithms, both perceptually(PI-minimization) and
objectively(PSNR-maximization) with fewer parameters.
