  The increased importance of mobile photography created a need for fast and
performant RAW image processing pipelines capable of producing good visual
results in spite of the mobile camera sensor limitations. While deep
learning-based approaches can efficiently solve this problem, their
computational requirements usually remain too large for high-resolution
on-device image processing. To address this limitation, we propose a novel
PyNET-V2 Mobile CNN architecture designed specifically for edge devices, being
able to process RAW 12MP photos directly on mobile phones under 1.5 second and
producing high perceptual photo quality. To train and to evaluate the
performance of the proposed solution, we use the real-world Fujifilm UltraISP
dataset consisting on thousands of RAW-RGB image pairs captured with a
professional medium-format 102MP Fujifilm camera and a popular Sony mobile
camera sensor. The results demonstrate that the PyNET-V2 Mobile model can
substantially surpass the quality of tradition ISP pipelines, while
outperforming the previously introduced neural network-based solutions designed
for fast image processing. Furthermore, we show that the proposed architecture
is also compatible with the latest mobile AI accelerators such as NPUs or APUs
that can be used to further reduce the latency of the model to as little as 0.5
second. The dataset, code and pre-trained models used in this paper are
available on the project website: https://github.com/gmalivenko/PyNET-v2
