Here, we describe our solution for the AIM-2019 Extreme Super-Resolution
Challenge, where we won the 1st place in terms of perceptual quality (MOS)
similar to the ground truth and achieved the 5th place in terms of
high-fidelity (PSNR). To tackle this challenge, we introduce the second
generation of MultiGrid BackProjection networks (MGBPv2) whose major
modifications make the system scalable and more general than its predecessor.
It combines the scalability of the multigrid algorithm and the performance of
iterative backprojections. In its original form, MGBP is limited to a small
number of parameters due to a strongly recursive structure. In MGBPv2, we make
full use of the multigrid recursion from the beginning of the network; we allow
different parameters in every module of the network; we simplify the main
modules; and finally, we allow adjustments of the number of network features
based on the scale of operation. For inference tasks, we introduce an
overlapping patch approach to further allow processing of very large images
(e.g. 8K). Our training strategies make use of a multiscale loss, combining
distortion and/or perception losses on the output as well as downscaled output
images. The final system can balance between high quality and high performance.

使用第二代 MultiGrid BackProjection 网络 (MGBPv2) 来解决 AIM-2019 极致超分辨率挑战，通过结合多重网格算法和迭代反向投影，使该系统可扩展和更普适，采用多尺度损失训练策略以及重叠块方法进行推断任务，最终实现解决高品质和高性能之间的平衡。