  Perceptual Extreme Super-Resolution for single image is extremely difficult,
because the texture details of different images vary greatly. To tackle this
difficulty, we develop a super resolution network with receptive field block
based on Enhanced SRGAN. We call our network RFB-ESRGAN. The key contributions
are listed as follows. First, for the purpose of extracting multi-scale
information and enhance the feature discriminability, we applied receptive
field block (RFB) to super resolution. RFB has achieved competitive results in
object detection and classification. Second, instead of using large convolution
kernels in multi-scale receptive field block, several small kernels are used in
RFB, which makes us be able to extract detailed features and reduce the
computation complexity. Third, we alternately use different upsampling methods
in the upsampling stage to reduce the high computation complexity and still
remain satisfactory performance. Fourth, we use the ensemble of 10 models of
different iteration to improve the robustness of model and reduce the noise
introduced by each individual model. Our experimental results show the superior
performance of RFB-ESRGAN. According to the preliminary results of NTIRE 2020
Perceptual Extreme Super-Resolution Challenge, our solution ranks first among
all the participants.
