Abstract: Image relighting is commonly used in image editing and data augmentation tasks.Existing image relighting methods suffer from estimating accurate shadows and obtaining consistent structures and clear texture when removing and rendering sha-dows in complex scenes.To address these issues,this paper proposes an image relighting network based on context-gated resi-duals and multiscale attention.Contextual gating residuals capture the long-range dependencies of pixels by aggregating local and global spatial context information,which maintains the consistency of shadow and lighting direction.Besides,gating mechanisms can effectively improve the network's ability to recover textures and structures.Multiscale attention increases the receptive field without losing resolution by iteratively extracting and aggregating features of different scales.It activates important features by concatenating channel attention and spatial attention,and suppresses the responses of irrelevant features.In this paper,lighting gradient loss is also proposed to obtain satisfactory visual images through efficiently learning the lighting gradients in all directions.Experimental results show that,compared with the current state-of-the-art methods,the proposed method improves PSNR and SSIM by 7.47% and 12.37%,respectively.

Key words: Image relighting, Contextual information, Gating mechanism, Lighting gradient, Attention