  RAW image datasets are more suitable than the standard RGB image datasets for
the ill-posed inverse problems in low-level vision, but not common in the
literature. There are also a few studies to focus on mapping sRGB images to RAW
format. Mapping from sRGB to RAW format could be a relevant domain for reverse
style transferring since the task is an ill-posed reversing problem. In this
study, we seek an answer to the question: Can the ISP operations be modeled as
the style factor in an end-to-end learning pipeline? To investigate this idea,
we propose a novel architecture, namely RST-ISP-Net, for learning to reverse
the ISP operations with the help of adaptive feature normalization. We
formulate this problem as a reverse style transferring and mostly follow the
practice used in the prior work. We have participated in the AIM Reversed ISP
challenge with our proposed architecture. Results indicate that the idea of
modeling disruptive or modifying factors as style is still valid, but further
improvements are required to be competitive in such a challenge.
