  Neural-networks based image restoration methods tend to use low-resolution
image patches for training. Although higher-resolution image patches can
provide more global information, state-of-the-art methods cannot utilize them
due to their huge GPU memory usage, as well as the instable training process.
However, plenty of studies have shown that global information is crucial for
image restoration tasks like image demosaicing and enhancing. In this work, we
propose a HighEr-Resolution Network (HERN) to fully learning global information
in high-resolution image patches. To achieve this, the HERN employs two
parallel paths to learn image features in two different resolutions,
respectively. By combining global-aware features and multi-scale features, our
HERN is able to learn global information with feasible GPU memory usage.
Besides, we introduce a progressive training method to solve the instability
issue and accelerate model convergence. On the task of image demosaicing and
enhancing, our HERN achieves state-of-the-art performance on the AIM2019 RAW to
RGB mapping challenge. The source code of our implementation is available at
https://github.com/MKFMIKU/RAW2RGBNet.
