  With the advent of deep learning methods replacing the ISP in transforming
sensor RAW readings into RGB images, numerous methodologies solidified into
real-life applications. Equally potent is the task of inverting this process
which will have applications in enhancing computational photography tasks that
are conducted in the RAW domain, addressing lack of available RAW data while
reaping from the benefits of performing tasks directly on sensor readings. This
paper's proposed methodology is a state-of-the-art solution to the task of RAW
reconstruction, and the multi-step refinement process integrating an
overexposure mask is novel in three ways: instead of from RGB to bayer, the
pipeline trains from RGB to demosaiced RAW allowing use of perceptual loss
functions; the multi-step processes has greatly enhanced the performance of the
baseline U-Net from start to end; the pipeline is a generalizable process of
refinement that can enhance other high performance methodologies that support
end-to-end learning.
