Human face image colorization with dual-scale attention U-Net

In order to colorize grayscale human face image and solve the problems of boundary leakage and detail loss in current colorization algorithms, we propose the dual-scale attention U-Net. First, on the basis of U-Net, we improve the original convolution block and select two convolution kernels of different sizes to extract features, which improves the ability of deep feature extraction for different scales. Second, integrating CBAM into skip connections helps network to focus on salient regions and suppress unnecessary regions while sharing features and reducing information loss. In addition, inspired by the field of image super resolution, MS-SSIM-L1 is adopted as the loss function, and the edge and texture information of the image can be taken into account when coloring. Experiments show that compared with other colorization algorithms, the proposed method not only reduces boundary leakage and detail loss, but also has outstanding performance in colorizing old photos of historical figures.