  Image inpainting is an important task in computer vision. As admirable
methods are presented, the inpainted image is getting closer to reality.
However, the result is still not good enough in the reconstructed texture and
structure based on human vision. Although recent advances in computer hardware
have enabled the development of larger and more complex models, there is still
a need for lightweight models that can be used by individuals and small-sized
institutions. Therefore, we propose a lightweight model that combines a
specialized transformer with a traditional convolutional neural network (CNN).
Furthermore, we have noticed most researchers only consider three primary
colors (RGB) in inpainted images, but we think this is not enough. So we
propose a new loss function to intensify color details. Extensive experiments
on commonly seen datasets (Places2 and CelebA) validate the efficacy of our
proposed model compared with other state-of-the-art methods.
  Index Terms: HSV color space, image inpainting, joint attention, stripe
window, transformer
