Jinxian Bai, Yao Fan^*, Zhiwei Zhao, Lizhi Zheng

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 999-1025, 2024, DOI:10.32604/cmc.2023.044612

Abstract Recently, deep learning-based image inpainting methods have made great strides in reconstructing damaged regions. However, these methods often struggle to produce satisfactory results when dealing with missing images with large holes, leading to distortions in the structure and blurring of textures. To address these problems, we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms. The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details. This method divides the inpainting task… More >

Peng Geng¹, Ji Lu¹, Ying Zhang^2,*, Simin Ma¹, Zhanzhong Tang², Jianhua Liu³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2001-2023, 2023, DOI:10.32604/cmes.2023.027127

Abstract In medical image segmentation task, convolutional neural networks (CNNs) are difficult to capture long-range dependencies, but transformers can model the long-range dependencies effectively. However, transformers have a flexible structure and seldom assume the structural bias of input data, so it is difficult for transformers to learn positional encoding of the medical images when using fewer images for training. To solve these problems, a dual branch structure is proposed. In one branch, Mix-Feed-Forward Network (Mix-FFN) and axial attention are adopted to capture long-range dependencies and keep the translation invariance of the model. Mix-FFN whose depth-wise convolutions can provide position information is… More >