  Deep learning-based single image super-resolution enables very fast and
high-visual-quality reconstruction. Recently, an enhanced super-resolution
based on generative adversarial network (ESRGAN) has achieved excellent
performance in terms of both qualitative and quantitative quality of the
reconstructed high-resolution image. In this paper, we propose to add one more
shortcut between two dense-blocks, as well as add shortcut between two
convolution layers inside a dense-block. With this simple strategy of adding
more shortcuts in the proposed network, it enables a faster learning process as
the gradient information can be back-propagated more easily. Based on the
improved ESRGAN, the dual reconstruction is proposed to learn different aspects
of the super-resolved image for judiciously enhancing the quality of the
reconstructed image. In practice, the super-resolution model is pre-trained
solely based on pixel distance, followed by fine-tuning the parameters in the
model based on adversarial loss and perceptual loss. Finally, we fuse two
different models by weighted-summing their parameters to obtain the final
super-resolution model. Experimental results demonstrated that the proposed
method achieves excellent performance in the real-world image super-resolution
challenge. We have also verified that the proposed dual reconstruction does
further improve the quality of the reconstructed image in terms of both PSNR
and SSIM.
