Cascaded refinement residual attention network for image outpainting

作者：Yang, Yizhong^*; Yao, Shanshan; Liu, Changjiang; Zhang, Zhang; Xie, Guangjun^*

来源：Multimedia Systems, 2024, 30(2): 68.

DOI：10.1007/s00530-024-01265-4

• Image outpainting
• Attention mechanism
• Deep learning
• Cascaded dilated convolution

The image outpainting based on deep learning shows good performance and has a wide range of applications in many fields. The previous image outpainting methods mostly used a single image as input. In this paper, we use the left and right images as input images, and expand the unknown region in the middle to generate an image that connects the left and right images to form a complete semantically smooth image. A cascaded refinement residual attention model for image outpainting is proposed. The Residual Channel-Spatial Attention (RCSA) module is designed to effectively learn image information about known regions. The Cascaded Dilated-conv (CDC) module is used to capture deep features, and more semantic information is obtained through dilated convolutions of different rates. The Refine on Features Aggregation (RFA) module connects the encoder and decoder to refine the result image for generating it clearer and smoother. Experimental results show that the proposed model is able to hallucinate more meaningful structures and vivid textures and achieve satisfactory results.