  Image demosaicing is problem of interpolating full- resolution color images
from raw sensor (color filter array) data. During last decade, deep neural
networks have been widely used in image restoration, and in particular, in
demosaicing, attaining significant performance improvement. In recent years,
vision transformers have been designed and successfully used in various
computer vision applications. One of the recent methods of image restoration
based on a Swin Transformer (ST), SwinIR, demonstrates state-of-the-art
performance with a smaller number of parameters than neural network-based
methods. Inspired by the success of SwinIR, we propose in this paper a novel
Swin Transformer-based network for image demosaicing, called RSTCANet. To
extract image features, RSTCANet stacks several residual Swin Transformer
Channel Attention blocks (RSTCAB), introducing the channel attention for each
two successive ST blocks. Extensive experiments demonstrate that RSTCANet out-
performs state-of-the-art image demosaicing methods, and has a smaller number
of parameters.
