  Recent research has highlighted improvements in high-quality imaging guided
by event cameras, with most of these efforts concentrating on the RGB domain.
However, these advancements frequently neglect the unique challenges introduced
by the inherent flaws in the sensor design of event cameras in the RAW domain.
Specifically, this sensor design results in the partial loss of pixel values,
posing new challenges for RAW domain processes like demosaicing. The challenge
intensifies as most research in the RAW domain is based on the premise that
each pixel contains a value, making the straightforward adaptation of these
methods to event camera demosaicing problematic. To end this, we present a
Swin-Transformer-based backbone and a pixel-focus loss function for demosaicing
with missing pixel values in RAW domain processing. Our core motivation is to
refine a general and widely applicable foundational model from the RGB domain
for RAW domain processing, thereby broadening the model's applicability within
the entire imaging process. Our method harnesses multi-scale processing and
space-to-depth techniques to ensure efficiency and reduce computing complexity.
We also proposed the Pixel-focus Loss function for network fine-tuning to
improve network convergence based on our discovery of a long-tailed
distribution in training loss. Our method has undergone validation on the MIPI
Demosaic Challenge dataset, with subsequent analytical experimentation
confirming its efficacy. All code and trained models are released here:
https://github.com/yunfanLu/ev-demosaic
