  As the popularity of mobile photography is growing constantly, lots of
efforts are being invested now into building complex hand-crafted camera ISP
solutions. In this work, we demonstrate that even the most sophisticated ISP
pipelines can be replaced with a single end-to-end deep learning model trained
without any prior knowledge about the sensor and optics used in a particular
device. For this, we present PyNET, a novel pyramidal CNN architecture designed
for fine-grained image restoration that implicitly learns to perform all ISP
steps such as image demosaicing, denoising, white balancing, color and contrast
correction, demoireing, etc. The model is trained to convert RAW Bayer data
obtained directly from mobile camera sensor into photos captured with a
professional high-end DSLR camera, making the solution independent of any
particular mobile ISP implementation. To validate the proposed approach on the
real data, we collected a large-scale dataset consisting of 10 thousand
full-resolution RAW-RGB image pairs captured in the wild with the Huawei P20
cameraphone (12.3 MP Sony Exmor IMX380 sensor) and Canon 5D Mark IV DSLR. The
experiments demonstrate that the proposed solution can easily get to the level
of the embedded P20's ISP pipeline that, unlike our approach, is combining the
data from two (RGB + B/W) camera sensors. The dataset, pre-trained models and
codes used in this paper are available on the project website.
