GCAM: lightweight image inpainting via group convolution and attention mechanism

Recently, image inpainting techniques tend to be more concerned with how to enhance the quality of restoration than with how to function on various platforms with limited processing power. In this paper, we propose a lightweight method that combines group convolution and attention mechanism to improve or replace the traditional convolution module. Group convolution was used to achieve multi-level image inpainting, and the authors proposed the rotating attention mechanism for allocation to deal with the issue of information mobility between channels in traditional convolution processing. The parallel discriminator structure was utilized throughout the network's overall design phase to guarantee both local and global consistency of the image inpainting process. The experimental results can demonstrate that, while the quality of image inpainting has been ensured, the proposed image inpainting network's inference time and resource usage are significantly lower than those of comparable lightweight approaches.